diff --git a/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/README.md b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/README.md new file mode 100644 index 0000000..57e6409 --- /dev/null +++ b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/README.md @@ -0,0 +1,63 @@ +# Tensorized Fourier Neural Operator for 3D Battery Heat Analysis + +This example builds off of the [Fourier Neural Operator for 3D Battery Heat Analysis](https://github.com/matlab-deep-learning/SciML-and-Physics-Informed-Machine-Learning-Examples/tree/main/battery-module-cooling-analysis-with-fourier-neural-operator) example to apply a Tensorized Fourier Neural Operator (TFNO) [1, 2] to heat analysis of a 3D battery module. The TFNO compresses the standard Fourier Neural Operator using tensorization, achieving 14.3x parameter reduction while maintaining accuracy. + +![](./images/prediction_vs_gt.png) +![](./images/absolute_error.png) + +## Setup + +Run the example by running [`tensorizedFourierNeuralOperatorForBatteryCoolingAnalysis.m`](./tensorizedFourierNeuralOperatorForBatteryCoolingAnalysis.m). + +## Requirements + +Requires: +- [MATLAB](https://www.mathworks.com/products/matlab.html) (R2025a or newer) +- [Deep Learning Toolbox™](https://www.mathworks.com/products/deep-learning.html) +- [Partial Differential Equation Toolbox™](https://mathworks.com/products/pde.html) +- [Parallel Computing Toolbox™](https://mathworks.com/products/parallel-computing.html) (for training on a GPU) + +## References +[1] Li, Zongyi, et al. "Fourier Neural Operator for Parametric Partial Differential Equations." +In International Conference on Learning Representations (2021). https://arxiv.org/pdf/2010.08895 + +[2] Kossaifi, Jean, et al. Kossaifi, Jean, et al. "Multi-Grid Tensorized Fourier Neural Operator for High-Resolution PDEs." +Transactions on Machine Learning Research (2024). https://arxiv.org/pdf/2310.00120 + +## Example Overview + +This example applies a 3D Tensorized Fourier Neural Operator (TFNO) to thermal analysis of a battery module composed of 20 cells. Given initial conditions (ambient temperature, convection, heat generation) at T=0, the TFNO predicts temperature distribution at T=10 minutes. + +### Architecture Modifications + +The TFNO includes two key modifications from the standard FNO: +1. **Transformer-like architecture**: Adds layer normalization, MLPs, and linear skip connections +2. **Tensorized spectral convolution**: Low-rank approximation of weight tensors + +### Key Hyperparameters + +- **Input channels**: 3 (ambient temperature, convection, heat generation) +- **Output channels**: 1 (temperature) +- **Number of modes**: 4 (retained Fourier modes per dimension) +- **Hidden channels**: 64 +- **FNO blocks**: 4 +- **Compression rank**: 0.05 (5% of original parameters in spectral layers) +- **Grid resolution**: 32×32×32 + +### Performance + +- **Inference speed**: 88ms per sample (batch size 1) on NVIDIA RTX 2080 Ti GPU and 230ms on Intel Xeon CPU (136x faster than FEM solver, 1.15x faster than the architecture from the prior [FNO example](https://github.com/matlab-deep-learning/SciML-and-Physics-Informed-Machine-Learning-Examples/tree/main/battery-module-cooling-analysis-with-fourier-neural-operator)) + - The speedup may be more pronounced on larger problem domains, higher dimensional problems, and/or when running inference on memory -constrained devices +- **Relative L2 error**: 0.009% error on test set +- **Training time**: 5.75 hours for 1000 epochs +- **Parameter reduction**: From 3,263,809 to 227,521 parameters for a 14.35x reduction +- **Memory savings**: 2.74MB compressed model vs 23.01MB dense model + +### Considerations +The example here is one instance of a TFNO applied to battery thermal analysis. It is likely that the TFNO may be further optimized with negligible accuracy loss by: +- Experimenting with higher compression ratios (e.g., 0.01-0.03) to achieve even greater parameter reduction +- Reducing the number of hidden channel dimensions +- Reducing the number of FNO blocks + +--- +Copyright 2026 The MathWorks, Inc. diff --git a/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/createBatteryModuleGeometry.m b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/createBatteryModuleGeometry.m new file mode 100644 index 0000000..3eb1880 --- /dev/null +++ b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/createBatteryModuleGeometry.m @@ -0,0 +1,100 @@ +function [geomModule, domainIDs, boundaryIDs, volume, boundaryArea, ReferencePoint] = createBatteryModuleGeometry(numCellsInModule, cellWidth,cellThickness,tabThickness,tabWidth,cellHeight,tabHeight, connectorHeight ) +%% Uses Boolean geometry functionality in PDE Toolbox, which requires release R2025a or later. +% If you have an older version, use the helper function in this example: +% https://www.mathworks.com/help/pde/ug/battery-module-cooling-analysis-and-reduced-order-thermal-model.html + +% Copyright 2025 The MathWorks, Inc. + +% First, create a single pouch cell by unioning the cell, tab and connector +% Cell creation +cell1 = fegeometry(multicuboid(cellThickness,cellWidth,cellHeight)); +cell1 = translate(cell1,[cellThickness/2,cellWidth/2,0]); +% Tab creation +tab = fegeometry(multicuboid(tabThickness,tabWidth,tabHeight)); +tabLeft = translate(tab,[cellThickness/2,tabWidth,cellHeight]); +tabRight = translate(tab,[cellThickness/2,cellWidth-tabWidth,cellHeight]); +% Union tabs to cells +geomPouch = union(cell1, tabLeft, KeepBoundaries=true); +geomPouch = union(geomPouch, tabRight, KeepBoundaries=true); +% Connector creation +overhang = (cellThickness-tabThickness)/2; +connector = fegeometry(multicuboid(tabThickness+overhang,tabWidth,connectorHeight)); +connectorRight = translate(connector,[cellThickness/2+overhang/2,tabWidth,cellHeight+tabHeight]); +connectorLeft = translate(connector,[(cellThickness/2-overhang/2),cellWidth-tabWidth,cellHeight+tabHeight]); +% Union connectors to tabs +geomPouch = union(geomPouch,connectorLeft,KeepBoundaries=true); +geomPouch = union(geomPouch,connectorRight,KeepBoundaries=true); +% Scale and translate completed pouch cell to create mirrored cell +geomPouchMirrored = translate(scale(geomPouch,[-1 1 1]),[cellThickness,0,0]); +% Union individual pouches to create full module +% Union even-numbered pouch cells together (original cells) +geomForward = fegeometry; +for i = 0:2:numCellsInModule-1 + offset = cellThickness*i; + geom_to_append = translate(geomPouch,[offset,0,0]); + geomForward = union(geomForward,geom_to_append); +end +% Union odd-numbered pouch cells together (mirrored cells) +geomBackward = fegeometry; +for i = 1:2:numCellsInModule-1 + offset = cellThickness*i; + geom_to_append = translate(geomPouchMirrored,[offset,0,0]); + geomBackward = union(geomBackward,geom_to_append); +end +% Union to create completed geometry module +geomModule = union(geomForward,geomBackward,KeepBoundaries=true); +% Rotate and translate the geometry +geomModule = translate(scale(geomModule,[1 -1 1]),[0 cellWidth 0]); +% Mesh the geometry to use query functions for identifying cells and faces +geomModule = generateMesh(geomModule,GeometricOrder="linear"); +% Create Reference Points for each geometry future +ReferencePoint.Cell = [cellThickness/2,cellWidth/2,cellHeight/2]; +ReferencePoint.TabLeft = [cellThickness/2,tabWidth,cellHeight+tabHeight/2]; +ReferencePoint.TabRight = [cellThickness/2,cellWidth-tabWidth,cellHeight+tabHeight/2]; +ReferencePoint.ConnectorLeft = [cellThickness/2,tabWidth,cellHeight+tabHeight+connectorHeight/2]; +ReferencePoint.ConnectorRight = [cellThickness/2,cellWidth-tabWidth,cellHeight+tabHeight+connectorHeight/2]; +% Helper function to get the cell IDs belonging to cell, tab and connector +[~,~,t] = meshToPet(geomModule.Mesh); +elementDomain = t(end,:); +tr = triangulation(geomModule.Mesh.Elements',geomModule.Mesh.Nodes'); +getCellID = @(point,cellNumber) elementDomain(pointLocation(tr,point+(cellNumber(:)-1)*[cellThickness,0,0])); +% Helper function to get the volume of the cells, tabs, and connectors +getVolumeOneCell = @(geomCellID) geomModule.Mesh.volume(findElements(geomModule.Mesh,"region",Cell=geomCellID)); +getVolume = @(geomCellIDs) arrayfun(@(n) getVolumeOneCell(n),geomCellIDs); +% Initialize cell ID and volume structs +domainIDs(1:numCellsInModule) = struct(Cell=[], ... + TabLeft=[],TabRight=[], ... + ConnectorLeft=[],ConnectorRight=[]); +volume(1:numCellsInModule) = struct(Cell=[], ... + TabLeft=[],TabRight=[], ... + ConnectorLeft=[],ConnectorRight=[]); +% Helper function to get the IDs belonging to the left, right, front, back, top and bottom faces +getFaceID = @(offsetVal,offsetDirection,cellNumber) nearestFace(geomModule,... + ReferencePoint.Cell + offsetVal/2 .*offsetDirection ... % offset ref. point to face + + cellThickness*(cellNumber(:)-1)*[1,0,0]); % offset to cell +% Initialize face ID and area structs +boundaryIDs(1:numCellsInModule) = struct(FrontFace=[],BackFace=[], ... + RightFace=[],LeftFace=[], ... + TopFace=[],BottomFace=[]); +boundaryArea(1:numCellsInModule) = struct(FrontFace=[],BackFace=[], ... + RightFace=[],LeftFace=[], ... + TopFace=[],BottomFace=[]); +% Loop over cell, left tab, right tab, left connector, and right connector to get cell IDs and volumes +for part = string(fieldnames(domainIDs))' + partid = num2cell(getCellID(ReferencePoint.(part),1:numCellsInModule)); + [domainIDs.(part)] = partid{:}; + volumesPart = num2cell(getVolume([partid{:}])); + [volume.(part)] = volumesPart{:}; +end +% Loop over front, back, right, left, top, and bottom faces IDs and areas +dimensions = [cellThickness;cellThickness;cellWidth;cellWidth;cellHeight;cellHeight]; +vectors = [-1,0,0;1,0,0;0,1,0;0,-1,0;0,0,1;0,0,-1]; +areaFormula = [cellHeight*cellWidth;cellHeight*cellWidth;cellThickness*cellHeight;cellThickness*cellHeight;cellThickness*cellWidth - tabThickness*tabWidth;cellThickness*cellWidth - tabThickness*tabWidth]; +i = 1; +for face = string(fieldnames(boundaryIDs))' + faceid = num2cell(getFaceID(dimensions(i),vectors(i,:),1:numCellsInModule)); + [boundaryIDs.(face)] = faceid{:}; + areasFace = num2cell(areaFormula(i)*ones(1,numCellsInModule)); + [boundaryArea.(face)] = areasFace{:}; + i = i+1; +end \ No newline at end of file diff --git a/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/downloadSimulationData.m b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/downloadSimulationData.m new file mode 100644 index 0000000..5e9832a --- /dev/null +++ b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/downloadSimulationData.m @@ -0,0 +1,28 @@ +function downloadSimulationData(url,destination) +% The downloadSimulationData function downloads pregenerated simulation +% data for the 3D battery heat analysis problem. + +% Copyright 2026 The MathWorks, Inc. + + if ~exist(destination,"dir") + mkdir(destination); + end + + [~,name,filetype] = fileparts(url); + netFileFullPath = fullfile(destination,name+filetype); + + % Check for the existence of the file and download the file if it does not + % exist + if ~exist(netFileFullPath,"file") + disp("Downloading simulation data."); + disp("This can take several minutes to download..."); + websave(netFileFullPath,url); + + % If the file is a ZIP file, extract it + if filetype == ".zip" + unzip(netFileFullPath,destination) + end + disp("Done."); + + end +end \ No newline at end of file diff --git a/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/images/absolute_error.png b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/images/absolute_error.png new file mode 100644 index 0000000..e5b61f2 Binary files /dev/null and b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/images/absolute_error.png differ diff --git a/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/images/prediction_vs_gt.png b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/images/prediction_vs_gt.png new file mode 100644 index 0000000..3d8e781 Binary files /dev/null and b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/images/prediction_vs_gt.png differ diff --git a/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/lossFunctions/h1Norm.m b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/lossFunctions/h1Norm.m new file mode 100644 index 0000000..2918a6f --- /dev/null +++ b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/lossFunctions/h1Norm.m @@ -0,0 +1,166 @@ +function H1 = h1Norm(X, params) +%H1NORM - Compute H1 norm on a grid. +% H1 = H1NORM(X) computes the H1 norm of the input array X +% with default parameters. +% +% H1 = H1NORM(X, Name=Value) specifies additional options using +% one or more name-value arguments: +% +% Spacings - 1xD vector of grid spacings [Δ1, Δ2, ..., ΔD]. +% The default value is ones(1,D). +% +% IncludeL2 - If true, computes full H1 norm (L2 + gradient). +% If false, computes seminorm only (gradient). +% The default value is true. +% +% Reduction - Method for reducing the norm across batch. +% Options are 'mean', 'sum', or 'none'. +% The default value is 'mean'. +% +% Periodic - 1xD logical array indicating which spatial +% dimensions are periodic. The default value +% is true for all dimensions. +% +% SquareRoot - If false, returns the squared H1 norm. +% If true, returns the H1 norm. The default +% value is false. +% +% Normalize - If true, divides output by C*prod(S1, S2, ...). +% The default value is false. +% +% The H1 norm is defined as: +% ||u||_{H^1} = (||u||_{L^2}^2 + ||∇u||_{L^2}^2)^{1/2} +% where ||∇u||_{L^2}^2 = Σ_i ||∂u/∂x_i||_{L^2}^2. +% +% Input X must be a numeric array of size [B, C, S1, S2, ..., SD] +% where B is batch size, C is number of channels, and S1...SD are +% spatial dimensions. +% +% Gradients are estimated using central differences and one-sided +% differences at boundaries (unless periodic boundary conditions). +% +% Example: +% B=2; C=1; S1=64; S2=64; +% X = randn(B,C,S1,S2); +% H1 = h1Norm(X); +% +% Copyright 2026 The MathWorks, Inc. + + arguments + X dlarray {mustBeNumeric} + params.Spacings (1,:) double = [] + params.IncludeL2 (1,1) logical = true + params.Reduction (1,1) string {mustBeMember(params.Reduction, {'mean', 'sum', 'none'})} = "mean" + params.Periodic (1,:) logical = true + params.SquareRoot (1,1) logical = false + params.Normalize (1,1) logical = false + end + + sz = size(X); + nd = ndims(X); + if nd < 3 + error('Input must be at least [B, C, S1].'); + end + B = sz(1); + C = sz(2); + spatialSizes = sz(3:end); + D = numel(spatialSizes); + + if isempty(params.Spacings) + params.Spacings = ones(1, D); + else + if numel(params.Spacings) ~= D + error('params.Spacings must have length equal to the number of spatial dimensions (D).'); + end + end + + if isscalar(params.Periodic) + params.Periodic = repmat(params.Periodic, 1, D); + elseif numel(params.Periodic) ~= D + error('params.Periodic must be scalar or 1xD logical.'); + end + + % Initialize H1 as the L2 error, + if params.IncludeL2 + H1 = l2Norm(X, Reduction="none", SquareRoot=false, Normalize=false); + else + H1 = zeros(B, 1, 'like', X); + end + + % Reshape to [B*C, S1, S2, ... Sn] so that all batch, channel + % combinations are handled independently. + X = reshape(X, [B*C spatialSizes]); + + % Add the H1 seminorm using forward differences. + for d = 1:D + delta = params.Spacings(d); + + dm = 1 + d; % Dimension index of this spatial axis in reshaped X. + + % Central difference with wrap. + fd = (circshift(X, -1, dm) - circshift(X, 1, dm)) / (2 * delta); + + if ~params.Periodic(d) + % Replace first/last elements with forward/reverse differences. + + if min(spatialSizes) < 4 + error("Non-periodic dimensions require at least 4 grid points for 3rd-order differences."); + end + + fd = applyThirdOrderDifferenceAtBoundary(fd, X, dm, delta); + end + + fd = fd.^2; + + % Reshape back to original size. + fd = reshape(fd, sz); + + % Sum over channels and spatial dimensions, giving size of [B, 1]. + fd = sum(fd, 2:nd); + + % Accumulate per-batch sum. + H1 = H1 + fd; + end + + if params.SquareRoot + H1 = sqrt(H1); + end + + if params.Normalize + % Normalize by channels and number of spatial points + H1 = H1 / (C * prod(spatialSizes)); + end + + if strcmp(params.Reduction, "mean") + H1 = mean(H1, 1); + elseif strcmp(params.Reduction, "sum") + H1 = sum(H1, 1); + end +end + +function fd = applyThirdOrderDifferenceAtBoundary(fd, X, d, delta) + + % Get the indices of components for 3rd-order forward differences. + idx1 = makeIndex(ndims(fd), d, 1); + idx2 = makeIndex(ndims(fd), d, 2); + idx3 = makeIndex(ndims(fd), d, 3); + idx4 = makeIndex(ndims(fd), d, 4); + + % Apply 3rd-order forward differences at left boundary. + fd(idx1{:})= (-11*X(idx1{:}) + 18*X(idx2{:}) - 9*X(idx3{:}) + 2*X(idx4{:})) / (6 * delta); + + % Get the indices of components for 3rd-order backward differences. + sz = size(fd, d); + idx1 = makeIndex(ndims(fd), d, sz); + idx2 = makeIndex(ndims(fd), d, sz-1); + idx3 = makeIndex(ndims(fd), d, sz-2); + idx4 = makeIndex(ndims(fd), d, sz-3); + + % Apply 3rd-order backward differences at right boundary + fd(idx1{:}) = (-2*X(idx4{:}) + 9*X(idx3{:}) - 18*X(idx2{:}) + 11*X(idx1{:})) / (6 * delta); +end + +function idx = makeIndex(ndims, toChange, val) + idx = repmat({':'}, 1, ndims); + idx{toChange} = val; +end diff --git a/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/lossFunctions/l2Norm.m b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/lossFunctions/l2Norm.m new file mode 100644 index 0000000..a6af045 --- /dev/null +++ b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/lossFunctions/l2Norm.m @@ -0,0 +1,59 @@ +function L2 = l2Norm(X, params) +%L2NORM - Compute L2 norm on a grid. +% L2 = L2NORM(X) computes the L2 norm of the input array X +% with default parameters. +% +% L2 = L2NORM(X, Name=Value) specifies additional options using +% one or more name-value arguments: +% +% Reduction - Method for reducing the norm across batch. +% Options are 'mean', 'sum', or 'none'. +% The default value is 'mean'. +% +% SquareRoot - If false, returns the squared L2 norm. +% If true, returns the L2 norm. The default +% value is false. +% +% Normalize - If true, divides output by C*prod(S1, S2, ...). +% The default value is false. +% +% Input X must be a numeric array of size [B, C, S1, S2, ..., SD] +% where B is batch size, C is number of channels, and S1...SD are +% spatial dimensions. +% +% Example: +% B=2; C=1; S1=64; S2=64; +% X = randn(B,C,S1,S2); +% L2 = l2Norm(X); +% +% Copyright 2026 The MathWorks, Inc. + + arguments + X dlarray {mustBeNumeric} + params.Reduction (1,1) string {mustBeMember(params.Reduction, {'mean', 'sum', 'none'})} = "mean" + params.SquareRoot (1,1) logical = false + params.Normalize (1,1) logical = false + end + + sz = size(X); + + % Convert to BxCS + X = reshape(X, sz(1), []); + + L2 = sum(abs(X.^2), 2); % Bx1, abs() needed for complex values + + if params.SquareRoot + L2 = sqrt(L2); + end + + if params.Reduction == "mean" + L2 = mean(L2); + elseif params.Reduction == "sum" + L2 = sum(L2); + end + + if params.Normalize + L2 = L2/(prod(sz(2:end))); + end + +end \ No newline at end of file diff --git a/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/lossFunctions/permuteDimFirst.m b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/lossFunctions/permuteDimFirst.m new file mode 100644 index 0000000..e29f70c --- /dev/null +++ b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/lossFunctions/permuteDimFirst.m @@ -0,0 +1,11 @@ +function X = permuteDimFirst(X, dim) +%PERMUTEDIMFIRST - Permute specified dimension to be the first dimension. +% X = PERMUTEDIMFIRST(X, DIM) moves the dimension specified by DIM +% to the first position while maintaining the relative order of other +% dimensions. + fmt = dims(X); + Dim = finddim(X, dim); + permuteOrder = [Dim setdiff(1:ndims(X), Dim, 'stable')]; + X = permute(stripdims(X), permuteOrder); + X = dlarray(X, fmt); +end \ No newline at end of file diff --git a/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/lossFunctions/relativeH1Loss.m b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/lossFunctions/relativeH1Loss.m new file mode 100644 index 0000000..364a5e8 --- /dev/null +++ b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/lossFunctions/relativeH1Loss.m @@ -0,0 +1,103 @@ +function loss = relativeH1Loss(pred, gt, params) +%RELATIVEH1LOSS - Compute the relative H1 norm loss between predictions and ground truth. +% LOSS = RELATIVEH1LOSS(PRED, GT) computes the relative H1 norm loss +% between predicted values PRED and ground truth values GT with default +% parameters. +% +% LOSS = RELATIVEH1LOSS(PRED, GT, Name=Value) specifies additional options +% using one or more name-value arguments: +% +% Normalize - If true, normalizes the H1 norm. +% The default value is false. +% +% SpatialSizes - 1xD vector of physical domain sizes for each spatial +% dimension. The default value is ones(1,D). +% +% SquareRoot - If true, returns the square root of the norm. +% If false, returns the squared norm. +% The default value is false. +% +% Reduction - Method for reducing the loss across batch. +% Options are 'mean', 'sum', or 'none'. +% The default value is 'mean'. +% +% Periodic - 1xD logical array indicating which spatial +% dimensions are periodic. The default value +% is true for all dimensions. +% +% The relative H1 loss is defined as: +% loss = ||pred - gt||_{H^1} / ||gt||_{H^1} +% where the H1 norm measures both function values and their gradients. +% This was proposed by +% Czarnecki, Wojciech M., et al. "Sobolev Training for Neural Networks." +% Advances in Neural Information Processing Systems (2017). +% +% Inputs PRED and GT must be dlarrays of size [B, C, S1, S2, ..., SD] +% where B is batch size, C is number of channels, and S1...SD are +% spatial dimensions. +% +% The loss is calculated per sample in the batch and then reduced +% according to the Reduction parameter. +% +% Example: +% B=2; C=1; S1=64; S2=64; +% pred = dlarray(randn(B,C,S1,S2)); +% gt = dlarray(randn(B,C,S1,S2)); +% loss = relativeH1Loss(pred, gt); +% +% Copyright 2026 The MathWorks, Inc. + + arguments + pred dlarray + gt dlarray + params.Normalize (1,1) logical = false + params.SpatialSizes (1,:) double = [] + params.SquareRoot (1,1) logical = false + params.Reduction (1,1) string {mustBeMember(params.Reduction, {'mean', 'sum', 'none'})} = "mean" + params.Periodic (1,:) logical = true + end + + if ~isequal(size(pred), size(gt)) + error('pred and gt must have identical size.'); + end + + if isempty(params.SpatialSizes) + params.SpatialSizes = ones(1, ndims(gt) - 2); + elseif isscalar(params.SpatialSizes) + params.SpatialSizes = repmat(params.SpatialSizes, 1, ndims(gt) - 2); + elseif numel(params.SpatialSizes) ~= ndims(gt) - 2 + error('params.SpatialSizes must have length equal to the number of spatial dimensions.'); + end + + % Ensure that dimension order is [B, C, S1, S2, ... Sn]. + pred = permuteDimFirst(pred, "C"); + gt = permuteDimFirst(gt, "C"); + pred = permuteDimFirst(pred, "B"); + gt = permuteDimFirst(gt, "B"); + + sz = size(pred); + quadrature = params.SpatialSizes./sz(3:end); + + num = h1Norm(gt - pred, ... + Spacings=quadrature, ... + Reduction='none', ... + Normalize=params.Normalize, ... + SquareRoot=params.SquareRoot, ... + Periodic=params.Periodic); + + den = h1Norm(gt, ... + Spacings=quadrature, ... + Reduction='none', ... + Normalize=params.Normalize, ... + SquareRoot=params.SquareRoot, ... + Periodic=params.Periodic); + + loss = num./(den + eps); + + switch params.Reduction + case "mean" + loss = mean(loss); + case "sum" + loss = sum(loss); + end +end \ No newline at end of file diff --git a/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/lossFunctions/relativeL2Loss.m b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/lossFunctions/relativeL2Loss.m new file mode 100644 index 0000000..51a69f6 --- /dev/null +++ b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/lossFunctions/relativeL2Loss.m @@ -0,0 +1,75 @@ +function [loss, errL2, gtL2] = relativeL2Loss(pred, gt, params) +% RELATIVEL2LOSS - Compute the relative L2 loss between predictions and ground truth. +% LOSS = RELATIVEL2LOSS(PRED, GT) computes the relative L2 loss of the +% predicted values PRED against ground truth GT with default parameters. +% +% [LOSS, ERRL2, GTL2] = RELATIVEL2LOSS(PRED, GT, Name=Value) specifies +% additional options using one or more name-value arguments: +% +% Reduction - Method for reducing the loss across batch. +% Options are 'mean', 'sum', or 'none'. +% The default value is 'mean'. +% +% SquareRoot - If true, takes the square root of L2 norm. +% If false, uses squared L2 norm. +% The default value is false. +% +% Normalize - If true, normalizes the L2 norm. +% The default value is false. +% +% The relative L2 loss is defined as: +% loss = ||pred - gt||_{L^2} / ||gt||_{L^2} +% which is calculated per sample in the batch and then reduced. +% +% This loss function is useful for problems where the scale of the +% target values varies significantly. +% +% Inputs PRED and GT must be dlarrays of identical size. +% +% Outputs: +% LOSS - Relative L2 loss value +% ERRL2 - L2 norm of the error (gt - pred) +% GTL2 - L2 norm of the ground truth +% +% Example: +% pred = dlarray(randn(10, 5)); +% gt = dlarray(randn(10, 5)); +% loss = relativeL2Loss(pred, gt); +% +% Copyright 2026 The MathWorks, Inc. + + arguments + pred dlarray + gt dlarray + params.Reduction (1,1) string {mustBeMember(params.Reduction, {'mean', 'sum', 'none'})} = "mean" + params.SquareRoot (1,1) logical = false + params.Normalize (1,1) logical = false + end + + pred = permuteDimFirst(pred, "B"); + gt = permuteDimFirst(gt, "B"); + + if ~isequal(size(pred), size(gt)) + error('pred and gt must have identical size.'); + end + + err = gt - pred; + + errL2 = l2Norm(err, ... + Normalize=params.Normalize, ... + Reduction='none', ... + SquareRoot=params.SquareRoot); + gtL2 = l2Norm(gt, ... + Normalize=params.Normalize, ... + Reduction='none', ... + SquareRoot=params.SquareRoot); + + loss = errL2./(gtL2 + eps); + + switch params.Reduction + case "mean" + loss = mean(loss); + case "sum" + loss = sum(loss); + end +end \ No newline at end of file diff --git a/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tensorizedFourierNeuralOperatorForBatteryCoolingAnalysis.m b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tensorizedFourierNeuralOperatorForBatteryCoolingAnalysis.m new file mode 100644 index 0000000..11c03b3 --- /dev/null +++ b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tensorizedFourierNeuralOperatorForBatteryCoolingAnalysis.m @@ -0,0 +1,499 @@ +%[text] # Tensorized Fourier Neural Operator for 3D Battery Heat Evolution +%[text] This example builds off of the [Fourier Neural Operator for 3D Battery Heat Equation](https://github.com/matlab-deep-learning/SciML-and-Physics-Informed-Machine-Learning-Examples/tree/main/battery-module-cooling-analysis-with-fourier-neural-operator) example. In that example, a Fourier Neural Operator (FNO) \[[1](internal:M_2049)\] is applied to predict how heat spreads through the battery. Given the ambient temperature, convection, and heat generation of the battery at time T=0, the problem is to predict the temperature at time T=10 minutes. +%[text] An FNO is a neural network that learns to solve Partial Differential Equations (PDEs). The advantages of an FNO over traditional numerical PDE solvers include: +%[text] - **Reduced-Order Modeling (ROM):** FNOs may solve PDEs much faster than traditional numerical methods, and faster than other [ROM](https://www.mathworks.com/help/pde/ug/battery-module-cooling-analysis-and-reduced-order-thermal-model.html) methods not involving neural networks. +%[text] - **Learning directly from data:** FNOs learn from data without need an explicit representation of the governing PDE, enabling solutions to problems where the governing equations or boundary conditions are not known. +%[text] - **Generalization:** A single model can be trained to handle various initial conditions without retraining, such as different initial battery temperatures. In contrast, traditional methods must solve each setting separately. +%[text] - **Supporting multiple resolutions:** the "zero-shot super resolution" capabilities of FNOs, as described in section 5.4 of \[[1](https://openreview.net/pdf?id=c8P9NQVtmnO)\], enables an FNO to be trained and deployed on data with different domain discretizations. \ +%[text] In this example, we compress an FNO network via tensorization and solve the battery heat evolution problem. Tensorization breaks a large weight matrix into several smaller pieces that are cheaper to store and compute with. More formally, tensorization enables high dimensional tensors to be factored into components which together have fewer parameters than the original dense tensor. The compressed network, called a TFNO (Tensorized Fourier Neural Operator) \[[2](internal:M_01fc)\] has the following additional advantages: +%[text] - **Faster training:** TFNOs have fewer parameters than FNOs, and may converge with fewer iterations in some problem settings. +%[text] - **Faster inference:** On memory-constrained hardware, TFNOs may provide even faster approximation of PDE solutions than FNOs. +%[text] - **Lower memory consumption:** TFNOs use a smaller memory footprint than FNOs. This advantage combined with low latency is critical for iteration speed, real-time deployment, and hardware or energy-constrained applications. It also enables the TFNO to handle significantly larger spatial grids and higher‑dimensional inputs while staying within GPU memory. In this example, the compressed network uses 14.34x fewer parameters than the original FNO and has an 8.39x smaller memory footprint. +%[text] - **Improved generalization:** On some problems, TFNOs can improve accuracy, and across many problems and compression ratios, there is negligible accuracy loss compared to FNOs \[[2](internal:M_01fc)\]. \ +%[text] Compression via tensorization may be applied to any large weight tensors, including those in spectral convolution or convolution layers. The compression method \[[2](internal:M_01fc)\] applies to any FNO, and shows the most benefit on high-dimensional problems like the 3D problem in this example. +%[text] #### Example Outline +%[text] The sections are outlined below. The first section is the same as from the [FNO](https://github.com/matlab-deep-learning/SciML-and-Physics-Informed-Machine-Learning-Examples/tree/main/battery-module-cooling-analysis-with-fourier-neural-operator) example. Since generating the simulation data takes a long time, an option is provided to download pregenerated data by setting `loadSimulationData = true`. +%[text] 1. **Specify Battery Module Geometry and Generate Simulation Data:** Define the 3D shape of the battery and run simulations to produce temperature data over varying material and physical properties using the [Partial Differential Equation Toolbox](https://mathworks.com/products/pde.html). +%[text] 2. **Prepare Data for Training:** Discretize the simulation data to a regular grid of points, like those that `meshgrid` and `ndgrid` create, via interpolation. +%[text] 3. **Compress FNO using Tensorization:** Apply tensorization to shrink the FNO model, creating a TFNO. +%[text] 4. **Train the TFNO:** Learn the mapping from initial battery conditions at time T=0 to battery conditions at time T=10 minutes. +%[text] 5. **Test the Model:** Compare the TFNO's predictions against numerical simulation results from section 2 and assess the model's latency. +%[text] 6. **Visualize the Model Predictions:** Interpolate the model's outputs onto the battery geometry and compare against numerical simulations. +%[text] 7. **Conclusion:** Summary of results. \ +%% +%[text] Set `loadSimulationData=true` to download pregenerated simulation data for training. To regenerate the simulation data from scratch, set `loadSimulationData=false`. This will take about 40 minutes. +loadSimulationData = true; +if loadSimulationData + % The following 2 lines will be uncommented once the support files are up. + % pregeneratedSimulationDataURL = "https://ssd.mathworks.com/supportfiles/sciml/data/batteryHeatAnalysis.zip"; + % downloadSimuationData(pregeneratedSimulationDataURL, pwd); +end +%% +%[text] ## Specify Battery Module Geometry and Generate Simulation Data +%[text] The battery module is composed of repeated cells. The helper function `createBateryModuleGeometry` sets up the geometry. The battery is composed of 20 aligned cells, and various parameters specify the shape of those cells. +cellWidth = 150/1000; +cellThickness = 15/1000; +tabThickness = 10/1000; +tabWidth = 15/1000; +cellHeight = 100/1000; +tabHeight = 5/1000; +connectorHeight = 3/1000; + +numCellsInModule = 20; + +[geomModule,volumeIDs,boundaryIDs,volume,area,ReferencePoint] = ... + createBatteryModuleGeometry(numCellsInModule, ... + cellWidth, ... + cellThickness, ... + tabThickness, ... + tabWidth, ... + cellHeight, ... + tabHeight, ... + connectorHeight); +%[text] Create an [`femodel`](https://mathworks.com/help/pde/ug/femodel.html) from the geometry and visualize with [`pdemesh`](https://mathworks.com/help/pde/ug/femodel.pdemesh.html). +model = femodel(AnalysisType="thermalTransient", ... + Geometry=geomModule); + +model = generateMesh(model); +pdemesh(model) %[output:0be5d57d] +%% +%[text] This section sets up the material properties of the model following the [original example](https://mathworks.com/help/pde/ug/battery-module-cooling-analysis-and-reduced-order-thermal-model.html). +cellIDs = [volumeIDs.Cell]; +tabIDs = [volumeIDs.TabLeft,volumeIDs.TabRight]; +connectorIDs = [volumeIDs.ConnectorLeft,volumeIDs.ConnectorRight]; +bottomPlateFaces = [boundaryIDs.BottomFace]; + +cellThermalCond.inPlane = 80; +cellThermalCond.throughPlane = 2; +tabThermalCond = 386; +connectorThermalCond = 400; + +density.Cell = 780; +density.TabLeft = 2700; +density.TabRight = 2700; +density.ConnectorLeft = 540; +density.ConnectorRight = 540; + +spHeat.Cell = 785; +spHeat.TabLeft = 890; +spHeat.TabRight = 890; +spHeat.ConnectorLeft = 840; +spHeat.ConnectorRight = 840; + +model.MaterialProperties(cellIDs) = ... + materialProperties(ThermalConductivity= ... + [cellThermalCond.throughPlane + cellThermalCond.inPlane + cellThermalCond.inPlane], ... + MassDensity=density.Cell, ... + SpecificHeat=spHeat.Cell); +model.MaterialProperties(tabIDs) = ... + materialProperties(ThermalConductivity=tabThermalCond, ... + MassDensity=density.TabLeft, ... + SpecificHeat=spHeat.TabLeft); +model.MaterialProperties(connectorIDs) = ... + materialProperties(ThermalConductivity=connectorThermalCond, ... + MassDensity=density.ConnectorLeft, ... + SpecificHeat=spHeat.ConnectorLeft); +%[text] Let the following 3 properties vary, the ambient temperature, the convection through the front and back of the module, and the heat generated by the cells. +numSamples = 6; +ambientTemperatureMin = 280; +ambientTemperatureMax = 300; +ambientTemperature = linspace(ambientTemperatureMin, ambientTemperatureMax, numSamples); + +frontBackConvectionMin = 10; +frontBackConvectionMax = 20; +frontBackConvection = linspace(frontBackConvectionMin, frontBackConvectionMax, numSamples); + +heatGenerationMin = 10; +heatGenerationMax = 20; +heatGeneration = linspace(heatGenerationMin, heatGenerationMax, numSamples); +%[text] Use `ndgrid` to create all combinations of these parameters. +[ambientTemperature, frontBackConvection, heatGeneration] = ndgrid(ambientTemperature, frontBackConvection,heatGeneration); +ambientTemperature = reshape(ambientTemperature,[],1); +frontBackConvection = reshape(frontBackConvection,[],1); +heatGeneration = reshape(heatGeneration,[],1); +params = cat(2,ambientTemperature,frontBackConvection,heatGeneration); +%[text] Loop through each parameter combination, specify the appropriate properties on the model, and solve. The simulation here solves up to time $T = 600$, i.e. 10 minutes. +%[text] This step can take over 40 minutes to solve all of the instances of the PDE. It is recommended you save the `results` variable if you intend to run this example multiple times using the command `save("pregeneratedSimulationData", "results","-v7.3")`, using `"-v7.3"` as the file is 1.96GB. If `loadSimulationData=true`, then pregenerated data will be used instead of running the simulation. +if loadSimulationData %[output:group:02e29dce] + load("pregeneratedSimulationData"); +else + results = cell(size(params,1),1); + T = 60*10; + timeVector = 0:60:T; + for i = 1:size(params,1) + model.FaceLoad([boundaryIDs(1).FrontFace, ... + boundaryIDs(end).BackFace]) = ... + faceLoad(ConvectionCoefficient=params(i,2), ... + AmbientTemperature=params(i,1)); + + nominalHeatGen = params(i,3)/volume(1).Cell; + model.CellLoad(cellIDs) = cellLoad(Heat=nominalHeatGen); + + model.CellIC = cellIC(Temperature=params(i,1)); + + results{i} = solve(model,timeVector); %[output:26740b63] + end + % save("pregeneratedSimulationData", "results", "-v7.3") this line will be removed +end %[output:group:02e29dce] +%% +%[text] ## Prepare data for training +%[text] The geometry in this example can be well approximated by a regular grid discretization without too much discretization error. +%[text] Get the bounds of the mesh. +XYZ = geomModule.Mesh.Nodes; +Xmin = min(XYZ,[],2); +Xmax = max(XYZ,[],2); +xmin = Xmin(1,:); +xmax = Xmax(1,:); +ymin = Xmin(2,:); +ymax = Xmax(2,:); +zmin = Xmin(3,:); +zmax = Xmax(3,:); +%[text] Create grid coordinates. This example discretizes the domain onto a $32 \\times 32 \\times 32$ grid. +n = 32; +x = linspace(xmin,xmax,n); +y = linspace(ymin,ymax,n); +z = linspace(zmin,zmax,n); +[X,Y,Z] = meshgrid(x,y,z); +Xflat = reshape(X,[],1); +Yflat = reshape(Y,[],1); +Zflat = reshape(Z,[],1); +%[text] At each $(x\_i,y\_i,z\_i)$ on the grid there are 3 material and physical properties that we let vary above: the ambient temperature $T\_i$, the convection $P\_i$ and the heat generation $Q\_i$ at the point $(x\_i, y\_i, z\_i)$. These will be the input features $u$. The target $v$ is the temperature at $(x\_i,y\_i,z\_i)$ at the end time of the simulation. +%[text] For a given instance of the parameters, `params(i,:)` corresponds to an ambient temperature $T\_i$, convection $P\_i$ and heat generation $Q\_i$ specified at the chosen vertices above. We can then consider $u\_i(x,y,z) = (T\_i(x,y,z),P\_i(x,y,z),Q\_i(x,y,z))$ as a function representation of the input data, where $T\_i, P\_i, Q\_i$ have been extended to take the value $0$ at all vertices besides those specified above. In this sense each of `results{i}` contains to a discretization of $u\_i$ onto the mesh vertices. The temperature $v\_i$ can be considered similarly. +%[text] We can interpolate the mesh discretization onto a regular grid or $32 \\times 32 \\times 32$ points. This will give us a multi-dimensional array `U` where `U(i,j,k,p,n)` corresponds to the `p`-th feature of the `n`-th observation at point $(x\_i,y\_j,z\_k)$. +%[text] The target temperature can be interpolated using [`interpolateTemperature`](https://uk.mathworks.com/help/pde/ug/pde.steadystatethermalresults.interpolatetemperature.html)`.T`he other parameters are interpolated using [`scatteredInterpolant`](https://uk.mathworks.com/help/matlab/ref/scatteredinterpolant.html). For the `scatteredInterpolant` approach, we use [`findNodes`](https://uk.mathworks.com/help/pde/ug/pde.femesh.findnodes.html) to get the node indices where material properties were specified, and interpolate a function defined to have the specified property value at those nodes, and $0$ elsewhere. The ambient temperature is treated as a constant feature across space. +%[text] The `interpolateTemperature` function may return `NaN` at coordinates that are actually extrapolations. This occurs because the grid coordinates may lie outside the geometry. In this case this is due to the tabs on the battery module which extend in the $z$-dimension. The approach here is to impute the `NaN` values by projecting up from the last non `NaN` value in the $z$ dimension. +%[text] As above, this interpolation step may take some time and it is advisable to save the data with a command such as `save("interpolatedData","U","V")` if you intend to run this example multiple times. If `loadSimulationData=true`, then pregenerated interpolated data will be used instead of performing the interpolation. +if loadSimulationData + load("interpolatedData"); +else + U = zeros(n,n,n,3,size(params,1)); + V = zeros(n,n,n,1,size(params,1)); + + for i = 1:size(params,1) + result = results{i}; + + % Interpolate the final temperature onto the regular grid + tidx = length(result.SolutionTimes); + T = result.interpolateTemperature(Xflat,Yflat,Zflat,tidx); + T = reshape(T,n,n,n); + + % Find the nan-s and impute along the z-dimension + nans = isnan(T); + for j = 1:n + for k = 1:n + idx = find(nans(j,k,:),1); + T(j,k,idx:end) = T(j,k,idx-1); + end + end + + % Specify the target + V(:,:,:,:,i) = T; + + % Specify functions for the convection discretised + % onto the nodes. + % Get the nodes for the face loads + convection = zeros(size(result.Mesh.Nodes,2),1); + boundary = findNodes(result.Mesh,"region",Face = [boundaryIDs(1).FrontFace,boundaryIDs(end).BackFace]); + convection(boundary) = params(i,2); + + % Interpolate the convection to the grid points + F = scatteredInterpolant(result.Mesh.Nodes.', convection); + convectionGrid = F(X,Y,Z); + + % Similarly interpolate the heat generation. + heatGen = zeros(size(result.Mesh.Nodes,2),1); + cells = findNodes(result.Mesh,"region",Cell=cellIDs); + heatGen(cells) = params(i,3); + G = scatteredInterpolant(result.Mesh.Nodes.',heatGen); + heatGenGrid = G(X,Y,Z); + + % Specify the ambient temperature as a constant feature, and append the + % convection and heat generation interpolated features. + ambientTemperature = repmat(params(i,1),size(convectionGrid)); + U(:,:,:,:,i) = cat(4,ambientTemperature,convectionGrid,heatGenGrid); + end + % save("interpolatedData","U","V") this line will be removed +end +%% +%[text] Split the data into training, validation, and testing datasets. Use an 80/10/10 split. +[idxTrain, idxVal, idxTest] = trainingPartitions(size(U, 5), [0.8 0.1 0.1]); +Utrain = U(:, :, :, :, idxTrain); +Vtrain = V(:, :, :, :, idxTrain); + +Uval = U(:, :, :, :, idxVal); +Vval = V(:, :, :, :, idxVal); + +Utest = U(:, :, :, :, idxTest); +Vtest = V(:, :, :, :, idxTest); +%[text] Normalize the input data and ground‑truth labels using min–max normalization. This rescales all features into a consistent range, making the data easier for the network to learn from. +umax = max(Utrain,[],[1,2,3,5]); +umin = min(Utrain,[],[1,2,3,5]); +vmax = max(Vtrain,[],[1,2,3,5]); +vmin = min(Vtrain,[],[1,2,3,5]); + +epsilon = eps; +Utrain = (Utrain - umin)./(umax - umin + epsilon); +Vtrain = (Vtrain - vmin)./(vmax - vmin + epsilon); +Uval = (Uval - umin)./(umax - umin + epsilon); +Vval = (Vval - vmin)./(vmax - vmin + epsilon); +Utest = (Utest - umin)./(umax - umin + epsilon); +Vtest = (Vtest - vmin)./(vmax - vmin + epsilon); + +Utrain = dlarray(Utrain, "SSSCB"); +Vtrain = dlarray(Vtrain, "SSSCB"); +Uval = dlarray(Uval, "SSSCB"); +Vval = dlarray(Vval, "SSSCB"); +Utest = dlarray(Utest, "SSSCB"); +Vtest = dlarray(Vtest, "SSSCB"); +%% +%[text] ## **Compress FNO using Tensorization** +%[text] We can use a Tensorized Fourier Neural Operator (TFNO) \[[2](internal:M_01fc)\] to solve this initial value problem. The TFNO in this example includes two modifications from the FNO defined in the prior [Battery Heat Diffusion example](https://github.com/matlab-deep-learning/SciML-and-Physics-Informed-Machine-Learning-Examples/tree/main/battery-module-cooling-analysis-with-fourier-neural-operator): +%[text] 1. Architecture modification of the standard FNO backbone from the initial paper \[[1](internal:M_2049)\] which improves performance. This adds layer normalization, an MLP in the spatial domain, and linear skip connections. +%[text] 2. Tensorization of FNO spectral convolution weights. This is a low-rank approximation compression technique. \ +%[text] Here are the TFNO architectural hyperparameters along with the value to be used in this example: +%[text:table]{"columnWidths":[-1,-1,763],"ignoreHeader":true} +%[text] | Hyperparameter | Value | Explanation | +%[text] | --- | --- | --- | +%[text] | Number of dimensions, $N${"editStyle":"visual"} | 3 |

We are modeling heat diffusion over 3 spatial dimensions and predicting the temperature at a single point in the future.

If we were to model temperature over time, then an additional dimension would be required. If we were to only model

the temperature of a single cross section of the battery, which is a 2D plane, then the number of dimensions would be 2.

| +%[text] | Number of input channels, $C\_{\\mathrm{in}}${"editStyle":"visual"} | 3 |

The input data in each spatial location includes ambient temperature, convection, and heat generation, for 3 total input

channels. Another example with 3 input channels could be modeling a 3D velocity field for fluid flow prediction,

represented by u, v, and w components.

| +%[text] | Number of output channels, $C\_{\\mathrm{out}}${"editStyle":"visual"} | 1 |

The network will predict the temperature in each spatial location. If we wanted to predict multiple values at each output

spatial location, such as temperature and other physical quantities like pressure or material phase, we would configure

the model to output one channel per predicted field.

| +%[text] | Number of modes, $M${"editStyle":"visual"} | 4 |

The number of retained low‑frequency Fourier modes in each spatial dimension used by the spectral convolution layer

to perform the global convolution in the frequency domain. In practice, values in the range 4 to 32 work well, with fewer

modes needed in higher dimensional problems and for less complicated PDEs.

| +%[text] | Number of hidden channels, $C\_{\\mathrm{hidden}}${"editStyle":"visual"} | 64 |

The number of channels in the hidden layers of the TFNO. This is a knob to control the representational capacity of the

network, where higher values are needed for more complicated PDEs. Values in the range 16 to 64 tend to work well.

| +%[text] | Number of FNO blocks, $L${"editStyle":"visual"} | 4 |

The number of sequential blocks in the architecture. Each block contains one spectral convolution layer. Values in the

range of 2 to 6 are reasonable for most problems. The size of the network is proportional to the number of FNO blocks.

| +%[text] | Compression Rank, $\\mathrm{R}\\mathrm{a}\\mathrm{n}\\mathrm{k}${"editStyle":"visual"} | 0.05 |

The approximate fraction of learnables in the spectral convolution layers to use when training. This controls the amount

of compression - the reduction in the number of learnables and memory footprint.

| +%[text:table] +%[text] +%[text] #### Architecture Modification +%[text] The modified architecture adds layer normalization, a multilayer perceptron, and linear skip connections. +%[text]{"align":"center"} ![](text:image:1ae8) +%[text]{"align":"center"} Architecture of an FNO block. This diagram is reproducible by running this code section and then [`deepNetworkDesigner`](https://www.mathworks.com/help/deeplearning/ref/deepnetworkdesigner-app.html)`(net)` +%[text] #### Compression via Tensorization +%[text] The "full rank" or "dense" spectral convolution weight tensor, without any compression, is of size $\\left\\lbrack C\_{\\mathrm{hidden}} ,C\_{\\mathrm{hidden}} ,\\;M\_1 ,M\_2 ,M\_3 \\right\\rbrack${"editStyle":"visual"}, which is over 262,000 learnables for the current hyperparameter settings. That is just for one layer; for four layers, the spectral convolution parameters eclipse a million learnables. Compression is applied to the weight tensors in each spectral convolution layer via tensorization. For details, refer to the paper \[[2](internal:M_01fc)\]. +%[text] First, create a dense TFNO to see the amount of parameters in the full rank model. +inputChannels = 3; +outputChannels = 1; +numModes = 4; +hiddenChannels = 64; +numBlocks = 4; +spatialLimits = [xmin, xmax; ymin, ymax; zmin, zmax]; + +netDense = tfno3d(numModes, ... + hiddenChannels, ... + InChannels=inputChannels, ... + OutChannels = outputChannels, ... + NumBlocks=numBlocks, ... + SpatialLimits=spatialLimits); + +analysis = analyzeNetwork(netDense, Plots="none"); +denseLearnables = analysis.TotalLearnables %[output:4b14a168] +%[text] Now, set the compression to 0.05 and observe the new number of learnables. +compressionRank = 0.05; + +net = tfno3d(numModes, ... + hiddenChannels, ... + InChannels=inputChannels, ... + OutChannels = outputChannels, ... + SpectralRank=compressionRank, ... + NumBlocks=numBlocks, ... + SpatialLimits=spatialLimits); + +analysis = analyzeNetwork(net, Plots="none"); +compressedLearnables = analysis.TotalLearnables %[output:0d59505d] +compressionRatio = denseLearnables/compressedLearnables %[output:1265e6f2] +%[text] We see that by using 5% of learnables in the spectral convolution layers, we have reduced the number of learnables in the network by a factor of 14.35x. If you were to save the TFNO and FNO to MAT files, they would be 1.45MB and 23.01MB, respectively, for a 15.93x decrease in memory footprint. +%% +%[text] ## Train the TFNO +%[text] Set up the training hyperparameters using the [`trainingOptions`](https://www.mathworks.com/help/deeplearning/ref/trainingoptions.html) function: +%[text] - **Optimizer:** use the Adam optimizer as was done in the paper \[[2](internal:M_01fc)\]. +%[text] - **Plots:** set to `"training-progress"` so that a new window will appear and update continuously with training loss values by iteration. +%[text] - **Mini-Batch Size:** set to 16 so as to not overwhelm the GPU memory. Problems with smaller input data and TFNO may use a larger batch size, although the learning rate may need to be adjusted in tandem with the mini-batch size. +%[text] - **Data format:** the data is in spatial-channel-batch order. +%[text] - **Shuffle:** set to `"every-epoch"` to randomize the order that the model sees the data in each epoch. +%[text] - **Initial learning rate:** a value of 0.001 works well for this problem. +%[text] - **Epochs:** train for 1000 epochs, which 5-7 several hours. Smalller problems may require fewer epochs for convergence. \ +%[text] Train using the [`trainnet`](https://www.mathworks.com/help/deeplearning/ref/trainnet.html) function and the [`relativeH1Loss`](file:./lossFunctions/relativeH1Loss.m) loss function as is done in the paper \[[2](internal:M_01fc)\]. Alternatively, the built-in [`l2loss`](https://www.mathworks.com/help/deeplearning/ref/dlarray.l2loss.html) also works for this problem, as demonstrated by the prior [FNO](https://github.com/matlab-deep-learning/SciML-and-Physics-Informed-Machine-Learning-Examples/tree/main/battery-module-cooling-analysis-with-fourier-neural-operator) example. The L2 loss does a point-wise comparison between predictions and ground truth, while the relative H1 loss additionally encourages the model's predictions to be smooth and match the shape of the ground truth via a comparison of the prediction and ground truth's gradients. +%[text] The training was done on a 12GB NVIDIA GeForce RTX 2080 Ti GPU. Training for 1000 epochs took 6.66 hours with the relative H1 loss and 5.75 hours with the L2 loss. Training output and curve is shown below for the relative H1 loss, with a decreasing slope indicating that the model is improving on the heat analysis task. +%[text] As above it is advisable to save the trained network with a command such as `save("trained_model","net")` if you intend to re-use the model or re-run this example. +opts = trainingOptions("adam",... + Plots="training-progress",... + MiniBatchSize=16,... + InputDataFormats="SSSCB",... + TargetDataFormats="SSSCB",... + Shuffle="every-epoch",... + ValidationData = {Uval,Vval},... + ValidationFrequency=100,... + InitialLearnRate=0.001, ... + MaxEpochs = 1000); + +lossFcn = @(pred, gt) relativeH1Loss(pred, gt, Periodic=false); +[net, info] = trainnet(Utrain, Vtrain, net, lossFcn, opts); %[output:6dd5e07b] %[output:985f1150] +% save("allData_3_3", "-v7.3") this line will be removed +%% +%[text] ## Test the Model +%[text] Gather the inference time of the network over the test set using [`minibatchpredict`](https://mathworks.com/help/deeplearning/ref/minibatchpredict.html) and compare to the uncompressed FNO network. +function testLatency(net, X, netName, batchsize) + % GPU + reset(gpuDevice) + numSamples = size(X, ndims(X)); + XGPU = gpuArray(X); + % Warmup + minibatchpredict(net, XGPU, MiniBatchSize=batchsize, ExecutionEnvironment="gpu"); + + f = @() gather(minibatchpredict(net, XGPU, MiniBatchSize=batchsize, ExecutionEnvironment="gpu")); + endTime = gputimeit(f, 1); + gpuLatency = endTime/numSamples; + disp(netName + " Average GPU time per sample (" + numSamples + " samples, " ... + + "batch size " + batchsize + "): " + gpuLatency + " seconds."); + + % CPU - only do 10 inferences + numSamplesCPU = 10; + X = X(:, :, :, :, 1:numSamplesCPU); + f = @() minibatchpredict(net, X, MiniBatchSize=batchsize, ExecutionEnvironment="cpu"); + endTime = timeit(f, 1); + cpuLatency = endTime/numSamplesCPU; + disp(netName + " Average CPU time per sample (" + numSamplesCPU + " samples, " ... + + "batch size " + batchsize + "): " + cpuLatency + " seconds."); +end + +netDense = initialize(netDense, Utrain(:, :, :, :, 1)); +batchsize = 1; +testLatency(net, Utrain, "TFNO", batchsize); %[output:4ddeb2cd] +testLatency(netDense, Utrain, "FNO", batchsize); %[output:653878a4] +%[text] Because the hardware on which this example was tested (NVIDIA RTX 2080 TI GPU and Intel Xeon CPU) is compute-bound rather than memory-bound, we see approximately the same latency for FNO and TFNO. On memory-bound hardware, the TFNO may outperform the FNO because the model memory footprint is much smaller. +%% +%[text] Compare the train, validation, and test losses using the [`l2loss`](https://www.mathworks.com/help/deeplearning/ref/dlarray.l2loss.html) and [`relativeH1Loss`](file:./lossFunctions/relativeH1Loss.m) functions, setting `NormalizationFactor="all-elements"` in the L2 loss calculation for equal comparison with the prior [FNO](https://github.com/matlab-deep-learning/SciML-and-Physics-Informed-Machine-Learning-Examples/tree/main/battery-module-cooling-analysis-with-fourier-neural-operator) example. +trainPred = minibatchpredict(net, Utrain); +trainLossL2 = l2loss(trainPred, Vtrain, NormalizationFactor="all-elements"); +trainLossH1 = relativeH1Loss(trainPred, Vtrain, Periodic=false); + +valPred = minibatchpredict(net, Uval); +valLossL2 = l2loss(valPred, Vval, NormalizationFactor="all-elements"); +valLossH1 = relativeH1Loss(valPred, Vval, Periodic=false); + +testPred = minibatchpredict(net, Utest); +testLossL2 = l2loss(testPred, Vtest, NormalizationFactor="all-elements"); +testLossH1 = relativeH1Loss(testPred, Vtest, Periodic=false); +numTestImgs = numel(idxTest); + +l2vals = [extractdata(trainLossL2); extractdata(valLossL2); extractdata(testLossL2)]; +h1vals = [extractdata(trainLossH1); extractdata(valLossH1); extractdata(testLossH1)]; +rowNames = ["Train"; "Validation"; "Test"]; +varNames = ["L2 Loss", "Relative H1 Loss"]; +lossTable = table(l2vals, h1vals, RowNames=rowNames, VariableNames=varNames); +disp(lossTable) %[output:24a16dcc] +%[text] We see similar magnitude loss values for the train, validation, and test set, indicating that the model generalizes well. For comparison, the FNO from the prior [FNO](https://github.com/matlab-deep-learning/SciML-and-Physics-Informed-Machine-Learning-Examples/tree/main/battery-module-cooling-analysis-with-fourier-neural-operator) example achieves training loss of 1.3942e-05 and validation loss of 1.3226e-05, which is of a similar magnitude as the TFNO in this example. Although the relative change in validation loss between the TFNO and prior example is about 105%, the absolute values of the errors is very small on this problem, indicating that both methods are able to learn well. The absolute change is 1.39e-05, which is negligible. +%% +%[text] ## Visualize Model Predictions +%[text] Choose a validation observation to visualize and compare to ground truth simulation data. To do this, inverse the scaling applied before training and interpolate back onto the mesh using `griddedInterpolant`. +imageIdx =1; %[control:slider:6d2d]{"position":[11,12]} + +% Inverse the scaling applied before training. +pred = testPred(:,:,:,:,imageIdx).*(vmax - vmin + epsilon) + vmin; + +% griddedInterpolant wants the data to be in ndgrid format, which requires +% the following permutation. +P = [2,1,3]; +result = results{idxTest(imageIdx)}; +interpolation = griddedInterpolant(... + permute(X,P),... + permute(Y,P),... + permute(Z,P),... + permute(extractdata(squeeze(pred)),P),... + 'spline'); +interpolatedPrediction = interpolation(result.Mesh.Nodes.'); +trueSolution = result.Temperature(:,end); +absoluteError = abs(interpolatedPrediction - trueSolution); + +% Get limits +mintemp = min([trueSolution; interpolatedPrediction]); +maxtemp = max([trueSolution; interpolatedPrediction]); + +titlePrefix = "Test Sample " + string(imageIdx); + +figure %[output:1362bcbc] +tiledlayout(1, 2, TileSpacing="tight") %[output:1362bcbc] +nexttile %[output:1362bcbc] +pdeplot3D(result.Mesh, ColorMapData = trueSolution, FaceAlpha = 1); %[output:1362bcbc] +clim([mintemp,maxtemp]); %[output:1362bcbc] +title(titlePrefix + " True Solution") %[output:1362bcbc] + +nexttile %[output:1362bcbc] +pdeplot3D(result.Mesh, ColorMapData = interpolatedPrediction, FaceAlpha = 1); %[output:1362bcbc] +clim([mintemp,maxtemp]); %[output:1362bcbc] +title(titlePrefix + " Predicted Solution") %[output:1362bcbc] +figure %[output:419e71b4] +pdeplot3D(result.Mesh, ColorMapData = absoluteError, FaceAlpha = 1); %[output:419e71b4] +title(titlePrefix + " Absolute Error") %[output:419e71b4] +%% +%[text] ## **Conclusion** +%[text] This example demonstrates the advantages of the tensorized Fourier Neural Operator (TFNO) compared to the standard FNO when applied to a 3D battery heat analysis problem. Tensorization significantly reduces memory consumption while preserving the essential modeling capabilities of the FNO. The key observations are: +%[text] - **Training speed:** TFNO training speed and convergence are on par with the dense FNO, with both taking approximately 6.66 hours using the Relative H1 loss. When using the L2 loss, training takes around 5.75 hours. While training speed is comparable for this problem, TFNOs may converge faster in other architectures or data regimes. +%[text] - **Inference speed:** On the compute‑bound hardware used here (NVIDIA RTX 2080 Ti GPU and Intel Xeon CPU), TFNO and FNO achieve similar inference latency: ~82 ms on GPU and ~245 ms on CPU. However, because TFNO drastically reduces model size, it requires lower memory bandwidth and is expected to yield faster inference on memory‑constrained or bandwidth‑limited hardware. +%[text] - **Memory consumption:** The TFNO reduces the parameter count by 14.35× and reduces on‑disk storage by 15.93×, making it suitable for memory‑constrained environments such as embedded devices, edge accelerators, or large‑scale simulation pipelines. +%[text] - **Generalization and accuracy:** The TFNO achieves L2 and relative H1 losses of the same order of magnitude as the dense FNO. Although the FNO achieves slightly lower validation loss, the absolute difference (2.14×10⁻⁵) is negligible for this problem. This demonstrates that the TFNO largely preserves the FNO’s accuracy despite a 14.35× reduction in parameters. \ +%[text] This example uses a single hyperparameter configuration. Further tuning of the architecture or training settings may lead to additional improvements in accuracy or performance. +%[text] The TFNO also maintains the advantages of the FNO: +%[text] - **Reduced‑order modeling:** TFNO inference remains far faster than classical numerical methods, with solutions ~136× faster than the finite‑element solver shown in the ROM example. +%[text] - **Learning directly from data:** TFNO acts as a black‑box PDE solver, requiring no explicit formulation of governing equations. +%[text] - **Generalization across initial conditions:** A single trained TFNO handles varying initial states without retraining. +%[text] - **Zero‑shot super‑resolution:** TFNO preserves the FNO’s ability to infer solutions at unseen domain discretizations. \ +%[text] #### Applications of Tensorized Fourier Neural Operators +%[text] Tensorization may be applied as a drop-in modification to any Fourier Neural Operator architecture. Since it is a compression technique, it is particularly effective on problems with high number of dimensions, large spatial sizes, or when memory consumption or latency is critical to performance. +%[text] To apply a TFNO to a new application: +%[text] 1. Format the training data as tensors with spatial, channel, and batch dimensions. If the application involves time, treat it as another spatial dimension. Normalizing the data to unit scale typically stabilizes training. +%[text] 2. Initialize the model with reasonable hyperparameters. Use the table from the "Compress FNO using Tensorization" section as a guide. +%[text] 3. Call `trainnet` as in this example, exploring both L2 and Relative H1 losses to see which performs better on the target PDE. \ +%% +%[text] ### References +%[text] %[text:anchor:M_2049] \[1\] Li, Zongyi, Nikola Borislavov Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar. 2021. "Fourier Neural Operator for Parametric Partial Differential Equations." In *International Conference on Learning Representations*. [https://arxiv.org/pdf/2010.08895](https://arxiv.org/pdf/2010.08895). +%[text] %[text:anchor:M_01fc] \[2\] Kossaifi, Jean, Nikola Borisalov Kovachki, Kamyar Azizzadenesheli, and Anima Anandkumar. 2024. "Multi-Grid Tensorized Fourier Neural Operator for High-Resolution PDEs." *Transactions on Machine Learning Research*. [https://arxiv.org/pdf/2310.00120](https://arxiv.org/pdf/2310.00120). + +%[appendix]{"version":"1.0"} +%--- +%[metadata:view] +% data: {"layout":"inline"} +%--- +%[text:image:1ae8] +% data: 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","height":337,"width":560}} 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0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.020000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.030000s user + 0.000000s system = 0.030000s CPU (300.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.010000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.030000s user + 0.000000s system = 0.030000s CPU (300.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.020000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.000000s user + 0.010000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.000000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.030000s user + 0.000000s system = 0.030000s CPU (300.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.010000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.010000s system = 0.020000s CPU (200.0%)\n 0.000000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.010000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.010000s system = 0.020000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.000000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.020000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.010000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.010000s system = 0.020000s CPU (200.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.020000s user + 0.010000s system = 0.030000s CPU (300.0%)\n 0.010000s wall, 0.010000s user + 0.010000s system = 0.020000s CPU (200.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.000000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.010000s system = 0.020000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.030000s user + 0.000000s system = 0.030000s CPU (300.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.010000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.010000s system = 0.020000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.020000s user + 0.010000s system = 0.030000s CPU (300.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.010000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.000000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.010000s system = 0.030000s CPU (300.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (200.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.000000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (n\/a%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.020000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (50.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n 0.010000s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (100.0%)\n","truncated":false}} +%--- +%[output:4b14a168] +% data: {"dataType":"textualVariable","outputData":{"name":"denseLearnables","value":"3263809"}} +%--- +%[output:0d59505d] +% data: {"dataType":"textualVariable","outputData":{"name":"compressedLearnables","value":"227521"}} +%--- +%[output:1265e6f2] +% data: {"dataType":"textualVariable","outputData":{"name":"compressionRatio","value":"14.3451"}} +%--- +%[output:6dd5e07b] +% data: {"dataType":"text","outputData":{"text":" Iteration Epoch TimeElapsed LearnRate TrainingLoss ValidationLoss\n _________ _____ ___________ _________ ____________ ______________\n 0 0 00:00:28 0.001 60.261\n 1 1 00:00:30 0.001 24.271 \n 50 5 00:03:19 0.001 0.37127 \n 100 10 00:05:20 0.001 0.4057 0.43097\n 150 15 00:07:18 0.001 0.1737 \n 200 20 00:09:19 0.001 0.1244 0.24107\n 250 25 00:11:18 0.001 0.13088 \n 300 30 00:13:17 0.001 0.12965 0.14001\n 350 35 00:15:17 0.001 0.07783 \n 400 40 00:17:18 0.001 0.047211 0.047648\n 450 45 00:19:17 0.001 0.031388 \n 500 50 00:21:18 0.001 0.031169 0.033273\n 550 55 00:23:17 0.001 0.032593 \n 600 60 00:25:18 0.001 0.024581 0.026521\n 650 65 00:27:16 0.001 0.021573 \n 700 70 00:29:16 0.001 0.023925 0.024106\n 750 75 00:31:14 0.001 0.018956 \n 800 80 00:33:14 0.001 0.017675 0.019773\n 850 85 00:35:12 0.001 0.018426 \n 900 90 00:37:12 0.001 0.016463 0.018347\n 950 95 00:39:10 0.001 0.017853 \n 1000 100 00:41:10 0.001 0.016797 0.016625\n 1050 105 00:43:09 0.001 0.025645 \n 1100 110 00:45:10 0.001 0.014866 0.017776\n 1150 115 00:47:08 0.001 0.014837 \n 1200 120 00:49:08 0.001 0.013751 0.015331\n 1250 125 00:51:06 0.001 0.024358 \n 1300 130 00:53:05 0.001 0.014429 0.015478\n 1350 135 00:55:04 0.001 0.013746 \n 1400 140 00:57:04 0.001 0.013172 0.014038\n 1450 145 00:59:02 0.001 0.014043 \n 1500 150 01:01:01 0.001 0.013515 0.015114\n 1550 155 01:03:00 0.001 0.012048 \n 1600 160 01:04:59 0.001 0.014901 0.011909\n 1650 165 01:06:58 0.001 0.0099479 \n 1700 170 01:08:57 0.001 0.015287 0.012774\n 1750 175 01:10:56 0.001 0.013977 \n 1800 180 01:12:56 0.001 0.010398 0.011387\n 1850 185 01:14:55 0.001 0.0093512 \n 1900 190 01:16:54 0.001 0.01246 0.013015\n 1950 195 01:18:53 0.001 0.010291 \n 2000 200 01:20:52 0.001 0.0098177 0.010915\n 2050 205 01:22:51 0.001 0.0098157 \n 2100 210 01:24:50 0.001 0.0082963 0.010071\n 2150 215 01:26:49 0.001 0.01365 \n 2200 220 01:28:49 0.001 0.010801 0.010043\n 2250 225 01:30:47 0.001 0.011837 \n 2300 230 01:32:48 0.001 0.0091266 0.010202\n 2350 235 01:34:46 0.001 0.007907 \n 2400 240 01:36:46 0.001 0.0081196 0.0092662\n 2450 245 01:38:47 0.001 0.0095243 \n 2500 250 01:40:46 0.001 0.010623 0.010545\n 2550 255 01:42:45 0.001 0.01044 \n 2600 260 01:44:46 0.001 0.0082871 0.0095584\n 2650 265 01:46:45 0.001 0.0083696 \n 2700 270 01:48:44 0.001 0.0081652 0.0088106\n 2750 275 01:50:43 0.001 0.0092666 \n 2800 280 01:52:43 0.001 0.0075647 0.0088117\n 2850 285 01:54:41 0.001 0.0075999 \n 2900 290 01:56:41 0.001 0.0081333 0.0086014\n 2950 295 01:58:40 0.001 0.0077121 \n 3000 300 02:00:40 0.001 0.0073256 0.0081157\n 3050 305 02:02:39 0.001 0.017036 \n 3100 310 02:04:38 0.001 0.011642 0.0095177\n 3150 315 02:06:37 0.001 0.010395 \n 3200 320 02:08:37 0.001 0.0080004 0.0084365\n 3250 325 02:10:35 0.001 0.0080394 \n 3300 330 02:12:35 0.001 0.010334 0.0090816\n 3350 335 02:14:35 0.001 0.0081159 \n 3400 340 02:16:35 0.001 0.0070078 0.0084848\n 3450 345 02:18:33 0.001 0.0081946 \n 3500 350 02:20:33 0.001 0.0084879 0.0078117\n 3550 355 02:22:32 0.001 0.013036 \n 3600 360 02:24:31 0.001 0.0081287 0.0081973\n 3650 365 02:26:30 0.001 0.0074608 \n 3700 370 02:28:30 0.001 0.0082892 0.009027\n 3750 375 02:30:29 0.001 0.0072954 \n 3800 380 02:32:28 0.001 0.010463 0.012829\n 3850 385 02:34:27 0.001 0.0061259 \n 3900 390 02:36:28 0.001 0.0057172 0.0067407\n 3950 395 02:38:27 0.001 0.0063278 \n 4000 400 02:40:27 0.001 0.011201 0.013049\n 4050 405 02:42:25 0.001 0.0067767 \n 4100 410 02:44:25 0.001 0.0075638 0.008197\n 4150 415 02:46:25 0.001 0.0060861 \n 4200 420 02:48:24 0.001 0.006078 0.0067695\n 4250 425 02:50:23 0.001 0.0056374 \n 4300 430 02:52:23 0.001 0.0073099 0.0094042\n 4350 435 02:54:21 0.001 0.010834 \n 4400 440 02:56:22 0.001 0.0050558 0.0058552\n 4450 445 02:58:21 0.001 0.0048533 \n 4500 450 03:00:22 0.001 0.0043728 0.0053513\n 4550 455 03:02:20 0.001 0.0065683 \n 4600 460 03:04:20 0.001 0.0045603 0.0047346\n 4650 465 03:06:19 0.001 0.009002 \n 4700 470 03:08:19 0.001 0.0053215 0.0052966\n 4750 475 03:10:17 0.001 0.0038146 \n 4800 480 03:12:17 0.001 0.0073987 0.007494\n 4850 485 03:14:16 0.001 0.006599 \n 4900 490 03:16:17 0.001 0.0043574 0.00525\n 4950 495 03:18:17 0.001 0.0039108 \n 5000 500 03:20:17 0.001 0.0043534 0.0060733\n 5050 505 03:22:15 0.001 0.0058483 \n 5100 510 03:24:15 0.001 0.0045542 0.0044451\n 5150 515 03:26:14 0.001 0.0040892 \n 5200 520 03:28:14 0.001 0.0072321 0.0038623\n 5250 525 03:30:13 0.001 0.0044796 \n 5300 530 03:32:13 0.001 0.0037439 0.0035641\n 5350 535 03:34:12 0.001 0.0073402 \n 5400 540 03:36:13 0.001 0.0037201 0.0088819\n 5450 545 03:38:11 0.001 0.0034117 \n 5500 550 03:40:11 0.001 0.0029891 0.0034429\n 5550 555 03:42:10 0.001 0.0028857 \n 5600 560 03:44:11 0.001 0.0040761 0.0035348\n 5650 565 03:46:11 0.001 0.0052419 \n 5700 570 03:48:11 0.001 0.0031061 0.0037295\n 5750 575 03:50:10 0.001 0.0080891 \n 5800 580 03:52:10 0.001 0.003615 0.0060595\n 5850 585 03:54:08 0.001 0.0029752 \n 5900 590 03:56:08 0.001 0.0032775 0.0030555\n 5950 595 03:58:07 0.001 0.0099242 \n 6000 600 04:00:08 0.001 0.0023483 0.0030852\n 6050 605 04:02:07 0.001 0.0032266 \n 6100 610 04:04:07 0.001 0.0047462 0.004587\n 6150 615 04:06:07 0.001 0.003501 \n 6200 620 04:08:07 0.001 0.0038727 0.0042915\n 6250 625 04:10:06 0.001 0.0028496 \n 6300 630 04:12:06 0.001 0.0022532 0.0027454\n 6350 635 04:14:06 0.001 0.0053261 \n 6400 640 04:16:07 0.001 0.0034552 0.0034881\n 6450 645 04:18:07 0.001 0.0060756 \n 6500 650 04:20:07 0.001 0.0036051 0.0032783\n 6550 655 04:22:06 0.001 0.0035638 \n 6600 660 04:24:06 0.001 0.0034358 0.0044524\n 6650 665 04:26:05 0.001 0.013175 \n 6700 670 04:28:06 0.001 0.002717 0.0034271\n 6750 675 04:30:05 0.001 0.0046493 \n 6800 680 04:32:06 0.001 0.0053904 0.0034004\n 6850 685 04:34:05 0.001 0.0025286 \n 6900 690 04:36:05 0.001 0.0051189 0.0044588\n 6950 695 04:38:05 0.001 0.0023673 \n 7000 700 04:40:05 0.001 0.0023751 0.0028062\n 7050 705 04:42:04 0.001 0.0049821 \n 7100 710 04:44:05 0.001 0.0038949 0.00324\n 7150 715 04:46:04 0.001 0.0038382 \n 7200 720 04:48:05 0.001 0.002659 0.002923\n 7250 725 04:50:04 0.001 0.0081124 \n 7300 730 04:52:04 0.001 0.0039251 0.0045337\n 7350 735 04:54:03 0.001 0.0071899 \n 7400 740 04:56:04 0.001 0.0029257 0.0025918\n 7450 745 04:58:04 0.001 0.0017884 \n 7500 750 05:00:04 0.001 0.009705 0.01276\n 7550 755 05:02:04 0.001 0.0024747 \n 7600 760 05:04:04 0.001 0.002068 0.0031793\n 7650 765 05:06:03 0.001 0.010889 \n 7700 770 05:08:03 0.001 0.0057657 0.0076418\n 7750 775 05:10:02 0.001 0.0044902 \n 7800 780 05:12:03 0.001 0.0021407 0.0026384\n 7850 785 05:14:02 0.001 0.0024953 \n 7900 790 05:16:03 0.001 0.0022728 0.0025862\n 7950 795 05:18:03 0.001 0.0019755 \n 8000 800 05:20:04 0.001 0.030418 0.017324\n 8050 805 05:22:04 0.001 0.0037192 \n 8100 810 05:24:04 0.001 0.0021719 0.0022769\n 8150 815 05:26:04 0.001 0.0019855 \n 8200 820 05:28:04 0.001 0.0020848 0.0023897\n 8250 825 05:30:04 0.001 0.0031794 \n 8300 830 05:32:05 0.001 0.0020196 0.0022125\n 8350 835 05:34:05 0.001 0.0021372 \n 8400 840 05:36:05 0.001 0.011037 0.0025793\n 8450 845 05:38:05 0.001 0.0046257 \n 8500 850 05:40:05 0.001 0.0056934 0.003617\n 8550 855 05:42:05 0.001 0.0018612 \n 8600 860 05:44:06 0.001 0.0025049 0.0048492\n 8650 865 05:46:08 0.001 0.0024532 \n 8700 870 05:48:08 0.001 0.0016666 0.0019407\n 8750 875 05:50:08 0.001 0.0020152 \n 8800 880 05:52:09 0.001 0.0017477 0.001996\n 8850 885 05:54:09 0.001 0.0057274 \n 8900 890 05:56:09 0.001 0.037833 0.037869\n 8950 895 05:58:09 0.001 0.0043048 \n 9000 900 06:00:10 0.001 0.0021131 0.0031499\n 9050 905 06:02:09 0.001 0.0021217 \n 9100 910 06:04:11 0.001 0.0021898 0.0032426\n 9150 915 06:06:10 0.001 0.0023383 \n 9200 920 06:08:11 0.001 0.0018528 0.002447\n 9250 925 06:10:11 0.001 0.0020176 \n 9300 930 06:12:13 0.001 0.0018143 0.0020178\n 9350 935 06:14:12 0.001 0.0019403 \n 9400 940 06:16:14 0.001 0.0017705 0.0028735\n 9450 945 06:18:14 0.001 0.0019698 \n 9500 950 06:20:15 0.001 0.0029061 0.0097839\n 9550 955 06:22:15 0.001 0.0021993 \n 9600 960 06:24:15 0.001 0.0023128 0.0020529\n 9650 965 06:26:15 0.001 0.0023756 \n 9700 970 06:28:16 0.001 0.0079229 0.012049\n 9750 975 06:30:15 0.001 0.0047906 \n 9800 980 06:32:16 0.001 0.0023247 0.0022119\n 9850 985 06:34:15 0.001 0.0016318 \n 9900 990 06:36:16 0.001 0.002597 0.0079992\n 9950 995 06:38:16 0.001 0.0015946 \n 10000 1000 06:40:16 0.001 0.0020025 0.0030405\nTraining stopped: Max epochs completed\n","truncated":false}} +%--- +%[output:985f1150] +% data: 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\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+%--- +%[output:4ddeb2cd] +% data: {"dataType":"text","outputData":{"text":"TFNO Average GPU time per sample (172 samples, batch size 1): 0.084151 seconds.\nTFNO Average CPU time per sample (10 samples, batch size 1): 0.25523 seconds.\n","truncated":false}} +%--- +%[output:653878a4] +% data: {"dataType":"text","outputData":{"text":"FNO Average GPU time per sample (172 samples, batch size 1): 0.079924 seconds.\nFNO Average CPU time per sample (10 samples, batch size 1): 0.23986 seconds.\n","truncated":false}} +%--- +%[output:24a16dcc] +% data: {"dataType":"text","outputData":{"text":" L2 Loss<\/strong> Relative H1 Loss<\/strong>\n __________<\/strong> ________________<\/strong>\n\n Train <\/strong> 3.3332e-05 0.0016887 \n Validation<\/strong> 2.7144e-05 0.0018227 \n Test <\/strong> 3.4645e-05 0.0017234 \n\n","truncated":false}} +%--- +%[output:1362bcbc] +% data: 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","height":337,"width":560}} 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+%--- diff --git a/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/contractFactor.m b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/contractFactor.m new file mode 100644 index 0000000..a9d3ffc --- /dev/null +++ b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/contractFactor.m @@ -0,0 +1,38 @@ +function A = contractFactor(A, factor, mode) +%CONTRACTFACTOR - Contract a tensor along a specified mode with a factor matrix. +% CONTRACTFACTOR performs mode-n multiplication of a tensor A with a factor matrix. +% The operation contracts the tensor along the specified mode by multiplying with +% the transpose of the factor matrix. +% +% Inputs: +% A - Input tensor of arbitrary dimensions +% factor - Factor matrix to multiply with (size: [new_dim x old_dim]) +% mode - Mode/dimension along which to perform the contraction (1-indexed) +% +% Output: +% A - Contracted tensor with updated size along the specified mode +% +% The function reshapes the tensor to facilitate matrix multiplication using +% pagemtimes for efficient computation, then reshapes back to the original +% number of dimensions with the updated size along the contracted mode. + +% Copyright 2026 The MathWorks, Inc. + + arguments + A + factor (:, :) + mode (1, 1) double + end + + sz = size(A); + % Reshape to [prod(before) x sz(mode) x prod(after)] + A = reshape(A, prod(sz(1:mode-1)), sz(mode), []); + + % Multiply along the second dimension with pagemtimes + % new size = [prod(before) x original_size(i) x prod(after)] + A = pagemtimes(A, 'none', factor, 'transpose'); + + % Update size for this mode and reshape back to N-D + sz(mode) = size(factor, 1); + A = reshape(A, sz); +end \ No newline at end of file diff --git a/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/depthwiseConv3dLayer.m b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/depthwiseConv3dLayer.m new file mode 100644 index 0000000..2f65074 --- /dev/null +++ b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/depthwiseConv3dLayer.m @@ -0,0 +1,73 @@ +classdef depthwiseConv3dLayer < nnet.layer.Layer & ... + nnet.layer.Formattable & nnet.layer.Acceleratable %#codegen +%DEPTHWISECONV3DLAYER - Depthwise 3D convolution layer for channel-wise scaling. +% This layer performs element-wise multiplication of input channels with +% learnable weights, scaling each channel independently. +% It supports optional bias addition and is designed for use in neural +% network architectures requiring channel-wise feature modulation. +% +% Given input X of size (S, S, S, C, B), returns X .* W +% where W is of size (1, 1, 1, C, 1). + +% Copyright 2026 The MathWorks, Inc. + + properties (Learnable) + Weight + Bias + end + + properties + NumChannels + UseBias + end + + methods + function layer = depthwiseConv3dLayer(numChannels, args) + arguments + numChannels (1, 1) double + args.UseBias (1, 1) logical = false + args.Name (1, 1) string = "depthwiseConv" + end + + layer.NumChannels = numChannels; + layer.UseBias = args.UseBias; + layer.Name = args.Name; + end + + function layer = initialize(layer, layout) + if ~isempty(layer.Weight) + return + end + sdim = finddim(layout, 'S'); + cdim = finddim(layout, 'C'); + tdim = finddim(layout, 'T'); + + hasTimeDimension = ~isempty(tdim); + numSpatialDimensions = numel(sdim); + assertValidNumConvolutionDimensions(3, hasTimeDimension, numSpatialDimensions); + + % Check the input data has a channel dimension + assertInputHasChannelDim(1, cdim); + + % There are either SSSCB or SSTCB dims in unknown order + weightSize = ones(1, 5); + weightSize(cdim) = layout.Size(cdim); + + % Same initialization as convolution2Dlayer, from + % /matlab/toolbox/nnet/cnn/+nnet/+internal/+cnn/+layer/+learnable/+initializer/Normal.m + layer.Weight = dlarray(randn(weightSize), layout.Format) * 0.01; + if layer.UseBias + layer.Bias = dlarray(zeros(weightSize), layout.Format); + else + layer.Bias = []; + end + end + + function Z = predict(layer, X) + Z = X .* layer.Weight; + if layer.UseBias + Z = Z + layer.Bias; + end + end + end +end \ No newline at end of file diff --git a/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/fnoBlock3D.m b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/fnoBlock3D.m new file mode 100644 index 0000000..24b7972 --- /dev/null +++ b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/fnoBlock3D.m @@ -0,0 +1,84 @@ +function layer = fnoBlock3D(numModes,latentChannelSize,args) +%FNOBLOCK3D - Create a 3D Fourier Neural Operator (FNO) block. +% layer = FNOBLOCK3D(numModes, latentChannelSize) creates a 3D FNO block +% that combines spectral convolution in Fourier space with channel-wise +% MLPs for feature transformation. The block includes optional skip +% connections and normalization layers. It is returned as a networkLayer. +% +% Inputs: +% numModes - Number of Fourier modes to use in spectral convolution +% latentChannelSize - Number of channels in the latent representation +% +% Supported Name-Value pairs are: +% "NumMLPLayers" Number of MLP layers (default: 2) +% "MLPExpansion" Channel expansion factor for MLP (default: 0.5) +% "Name" Name for the layer (default: "") +% "LinearFNOSkip" Enable linear skip connection for FNO (default: true) +% "ChannelMLPSkip" Enable channel-wise skip connection (default: true) +% "Rank" Rank for spectral convolution (default: 1) +% "UseSpectralConvBias" Use bias in spectral convolution (default: true) +% "FuseSpectralConv" Directly contract spectral activations with tensorized weights (default: true) +% +% The block consists of: +% 1. Spectral convolution in Fourier domain +% 2. Layer normalization and residual connection +% 3. Channel-wise MLP with expansion and contraction +% 4. Optional skip connections for improved gradient flow + +% Copyright 2026 The MathWorks, Inc. + +arguments + numModes (1, 3) double {mustBePositive, mustBeInteger, mustBeFinite} + latentChannelSize (1, 1) double {mustBePositive, mustBeInteger, mustBeFinite} + args.NumMLPLayers (1, 1) double {mustBePositive, mustBeInteger, mustBeFinite} = 2 + args.MLPExpansion (1, 1) double {mustBePositive, mustBeLessThan(args.MLPExpansion, 1)} = 0.5 + args.Name (1, 1) string = "" + args.LinearFNOSkip (1, 1) logical = true + args.ChannelMLPSkip (1, 1) logical = true + args.Rank (1, 1) double {mustBeInRange(args.Rank, 0, 1)} = 1 + args.UseSpectralConvBias (1, 1) logical = true + args.FuseSpectralConv (1, 1) logical = true +end + + name = args.Name; + + net = dlnetwork; + + layers = [identityLayer(Name="in"), ... + tensorizedSpectralConv3dLayer(... + latentChannelSize, ... + numModes, ... + Rank=args.Rank, ... + UseBias=args.UseSpectralConvBias, ... + Name="specConv", ... + Fuse=args.FuseSpectralConv), ... + layerNormalizationLayer(Name="ln1"), ... + additionLayer(2, Name="add1"), ... + geluLayer(Name="gelu1"), ... + convolution3dLayer(1, latentChannelSize * args.MLPExpansion, Name="channelMLP1"), ... + geluLayer(Name="gelu2"), ... + convolution3dLayer(1, latentChannelSize, Name="channelMLP2"), ... + layerNormalizationLayer(Name="ln2"), ... + additionLayer(2, Name="add2"), ... + geluLayer(Name="gelu3")]; + + net = addLayers(net, layers); + + if args.LinearFNOSkip + net = addLayers(net, convolution3dLayer(1, latentChannelSize, Name="fnoSkip", BiasLearnRateFactor=0)); + net = connectLayers(net, "in", "fnoSkip"); + net = connectLayers(net, "fnoSkip", "add1/in2"); + else + net = connectLayers(net, "in", "add1/in2"); + end + + if args.ChannelMLPSkip + net = addLayers(net, depthwiseConv3dLayer(latentChannelSize, Name="channelSkip")); + net = connectLayers(net, "in", "channelSkip"); + net = connectLayers(net, "channelSkip", "add2/in2"); + else + net = connectLayers(net, "in", "add2/in2"); + end + + layer = networkLayer(net,Name=name); +end \ No newline at end of file diff --git a/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/fullTensor.m b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/fullTensor.m new file mode 100644 index 0000000..7b170dd --- /dev/null +++ b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/fullTensor.m @@ -0,0 +1,26 @@ +function Afull = fullTensor(core, factors) +% FULLTENSOR Reconstruct a full tensor from its Tucker decomposition. +% Afull = FULLTENSOR(core, factors) reconstructs a full tensor from +% its Tucker decomposition components. The Tucker decomposition represents +% a tensor as a core tensor multiplied by factor matrices along each mode. +% +% Inputs: +% core - N-dimensional core tensor +% factors - Cell array of length N containing factor matrices +% factors{i} is a matrix of size original_size(i) x size(core, i) +% +% Output: +% Afull - Reconstructed full tensor +% +% The function iteratively contracts the core tensor with each factor +% matrix along the corresponding mode to reconstruct the original tensor. + +% Copyright 2026 The MathWorks, Inc. + + coder.varsize('Afull'); + + Afull = core; + for mode = 1:ndims(Afull) + Afull = contractFactor(Afull, factors{mode}, mode); + end +end \ No newline at end of file diff --git a/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/iPositiveAndNegativeFrequencies.m b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/iPositiveAndNegativeFrequencies.m new file mode 100644 index 0000000..84ee523 --- /dev/null +++ b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/iPositiveAndNegativeFrequencies.m @@ -0,0 +1,30 @@ +function [pos,neg] = iPositiveAndNegativeFrequencies(N) +%IPOSITIVENEGATIVEFREQUENCIES - Indices for positive and negative FFT frequencies. +% [pos,neg] = IPOSITIVENEGATIVEFREQUENCIES(N) returns the indices into the +% positive and negative frequencies of a Fourier transform as computed by fft +% on a real valued input with N entries. +% +% Inputs: +% N - Number of entries in the real-valued input signal +% +% Outputs: +% pos - Indices of positive frequencies (2:(floor(N/2)+1)) +% neg - Indices of negative frequencies (N:-1:(ceil(N/2)+1)) +% +% For example: +% >> u = rand(N,1); +% >> uhat = fft(u); +% The entries of uhat are: +% uhat(1) -- the 0 frequency +% uhat(pos) -- the positive frequencies +% uhat(neg) -- the negative frequencies +% +% Note that the negative frequencies are indexed from the end of the array +% because uhat(N) corresponds to the negative of the same frequency as +% uhat(2). As such, pos(i) and neg(i) are associated frequencies. + +% Copyright 2026 The MathWorks, Inc. + +pos = 2:(floor(N/2)+1); +neg = N:-1:(ceil(N/2)+1); +end diff --git a/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/spatialEmbeddingLayer3D.m b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/spatialEmbeddingLayer3D.m new file mode 100644 index 0000000..c76c46e --- /dev/null +++ b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/spatialEmbeddingLayer3D.m @@ -0,0 +1,57 @@ +classdef spatialEmbeddingLayer3D < nnet.layer.Layer & ... + nnet.layer.Formattable & nnet.layer.Acceleratable %#codegen +%SPATIALEMBEDDINGLAYER3D - 3D spatial embedding layer. +% layer = SPATIALEMBEDDINGLAYER3D(spatialLimits) creates a 3D spatial +% embedding layer that adds position embeddings to input data based on +% specified spatial limits. The layer generates position values linearly +% spaced between the limits and is discretization invariant. It is returned +% as a layer object. +% +% Inputs: +% spatialLimits - 3x2 matrix specifying [min, max] spatial bounds for each dimension +% +% Supported Name-Value pairs are: +% "Name" Name for the layer (default: "depthwiseConv") + +% Copyright 2026 The MathWorks, Inc. + + properties + SpatialLimits + end + + methods + function layer = spatialEmbeddingLayer3D(spatialLimits, args) + arguments + spatialLimits (3, 2) double + args.Name (1, 1) string = "depthwiseConv" + end + + layer.SpatialLimits = spatialLimits; + layer.Name = args.Name; + end + + function Z = predict(layer, X) + sdim = finddim(X, 'S'); + sSize = size(X, sdim); + + S1 = linspace(layer.SpatialLimits(1, 1), ... + layer.SpatialLimits(1, 2), sSize(1)); + S2 = linspace(layer.SpatialLimits(2, 1), ... + layer.SpatialLimits(2, 2), sSize(2)); + S3 = linspace(layer.SpatialLimits(3, 1), ... + layer.SpatialLimits(3, 2), sSize(3)); + + [embedding1, embedding2, embedding3] = meshgrid(S1, S2, S3); + + embedding = zeros([sSize, 3], Like=X); + embedding(:, :, :, 1) = embedding1; + embedding(:, :, :, 2) = embedding2; + embedding(:, :, :, 3) = embedding3; + + bdim = finddim(X, 'B'); + bSize = size(X, bdim); + Z = repmat(embedding, [1, 1, 1, 1, bSize]); + Z = dlarray(Z, "SSSCB"); + end + end +end diff --git a/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/tensorizedSpectralConv3dLayer.m b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/tensorizedSpectralConv3dLayer.m new file mode 100644 index 0000000..5a161cf --- /dev/null +++ b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/tensorizedSpectralConv3dLayer.m @@ -0,0 +1,261 @@ +classdef tensorizedSpectralConv3dLayer < nnet.layer.Layer ... + & nnet.layer.Formattable ... + & nnet.layer.Acceleratable %#codegen +%TENSORIZEDSPECTRALCONV3DLAYER - 3D Spectral Convolution Layer. +% layer = TENSORIZEDSPECTRALCONV3DLAYER(outChannels, numModes) +% creates a spectral convolution 3d layer. outChannels +% specifies the number of channels in the layer output. +% numModes specifies the number of modes which are combined +% in Fourier space for each of the 2 spatial dimensions. +% +% layer = tensorizedSpectralConv3dLayer(outChannels, numModes, +% Name=Value) specifies additional options using one or more +% name-value arguments: +% +% Name - Name for the layer. The default value is "". +% +% Weights - Complex learnable array of size +% [inChannels, outChannels, numModes(1), numModes(2), numModes(3)]. +% The default value is []. +% +% Bias - Real learnable array of size [1, 1, 1, outChannels]. +% The default value is []. +% +% Rank - Ratio of stored parameter count to full +% parameter count. Default 1 for no +% compression. If less than 1, uses a Tucker +% decomposition to represent the full tensor. +% +% Fuse - If true, applies the spectral convolution +% using a fused contraction path that operates +% directly on the factorized (Tucker) +% representation without reconstructing the +% dense spectral weight tensor. This typically +% reduces memory and improves latency. If false, +% reconstructs the full spectral weight tensor +% from the factors and multiplies with the +% input in Fourier space. This can be slower +% and more memory intensive. + +% Copyright 2026 The MathWorks, Inc. + + properties + NumChannels + OutputSize + NumModes + Rank + UseBias + Fuse + end + + properties (Dependent) + Tensorized + end + + properties (Learnable) + Weights + Core + Factor1 + Factor2 + Factor3 + Factor4 + Factor5 + Bias + end + + methods + function this = tensorizedSpectralConv3dLayer(outChannels,numModes,args) + arguments + outChannels (1,1) double + numModes (1,3) double + args.Name {mustBeTextScalar} = "tensorizedSpectralConv3d" + args.Weights = [] + args.Bias double = [] + args.Rank (1,1) double {mustBeInRange(args.Rank, 0, 1)} = 1 + args.UseBias (1, 1) logical = true + args.Fuse (1, 1) logical = true + end + + this.OutputSize = outChannels; + this.NumModes = numModes; + this.Name = args.Name; + this.Weights = args.Weights; + this.Rank = args.Rank; + this.UseBias = args.UseBias; + this.Fuse = args.Fuse; + + if ~isempty(args.Weights) && this.Tensorized + error("Rank must be 1 when providing weights."); + end + end + + function b = get.Tensorized(this) + b = this.Rank < 1; + end + + function this = initialize(this, ndl) + inChannels = ndl.Size( finddim(ndl,'C') ); + outChannels = this.OutputSize; + numModes = this.NumModes; + numModes(2:end) = numModes(2:end)*2 - 1; + this.NumChannels = inChannels; + + if isempty(this.Weights) || isempty(this.Core) + if this.Tensorized + this = this.initializeTucker(inChannels, outChannels, numModes); + else + std = 1./(inChannels+outChannels); + this.Weights = std.*randn([inChannels outChannels numModes], "like", 1i); + end + end + + if isempty(this.Bias) && this.UseBias + this.Bias = zeros(1, 1, 1, this.OutputSize); + end + end + + function this = initializeTucker(this, inChannels, outChannels, numModes) + sz = [inChannels outChannels numModes]; + contractFactor = this.Rank^(1/nnz(sz~=1)); + newSz = max(round(sz.*contractFactor), 1); + + % Core is an N-D tensor that is smaller than full tensor size of + % (inChannels)x(outChannels)x(numModes(1))x(numModes(2)). + % There is 1 factor matrix per dimension in the core mapping + % from the full tensor size to the core size. + + this.Core = 1./(inChannels+outChannels).*rand(newSz, 'like', 1i); + + this.Factor1 = 1./(newSz(1)+sz(1)).*rand([sz(1), newSz(1)], 'like', 1i); + this.Factor2 = 1./(newSz(2)+sz(2)).*rand([sz(2), newSz(2)], 'like', 1i); + this.Factor3 = 1./(newSz(3)+sz(3)).*rand([sz(3), newSz(3)], 'like', 1i); + this.Factor4 = 1./(newSz(4)+sz(4)).*rand([sz(4), newSz(4)], 'like', 1i); + this.Factor5 = 1./(newSz(5)+sz(5)).*rand([sz(5), newSz(5)], 'like', 1i); + end + + function Xout = predict(this, X) + N1 = size(X, 1); + N2 = size(X, 2); + N3 = size(X, 3); + channelSize = size(X, 4); + batchSize = size(X, 5); + assert(channelSize == this.NumChannels); + Nm1 = this.NumModes(1); + Nm2 = this.NumModes(2); + Nm3 = this.NumModes(3); + d = dims(X); + + % Reshape into ConvDim1 x ConvDim2 x ConvDim3 x Channel x BatchedDims + X = reshape(X,N1,N2,N3,channelSize,[]); + + % Compute FFT. Second and third dimensions may have complex values. + X = real(X); + X = stripdims(X); + + X = fft(fft(fft(X, [], 3), [], 2), [], 1); + + % Truncate to NumModes frequencies. In the first dimension we + % don't require the negative frequencies due to conjugate + % symmetry. + xFreq = 1:Nm1; + yPos = 1:Nm2; + yNeg = (N2-Nm2+2):N2; + yFreq = union(yPos,yNeg); + zPos = 1:Nm3; + zNeg = (N3-Nm3+2):N3; + zFreq = union(zPos,zNeg); + X = X(xFreq,yFreq,zFreq,:,:); + + % Permute the channel dimension to the front. + X = permute(X,[4,5,1,2,3]); + + if this.Tensorized && this.Fuse + % Directly contract X with the Tucker decomposition. + X = tuckerContract(X, this.Core, {this.Factor1, ... + this.Factor2, ... + this.Factor3, ... + this.Factor4, ... + this.Factor5}); + else + % Use dense weights, reconstructing if needed. + if this.Tensorized + % Reconstruct dense tensor from Tucker decomposition. + weights = fullTensor(this.Core, {this.Factor1, ... + this.Factor2, ... + this.Factor3, ... + this.Factor4, ... + this.Factor5}); + else + weights = this.Weights; + end + + % Potentially the weights can have too many modes in the 2nd + % and 3rd dimension when NumModes was chose maximally and the + % input convolution size is even. + weights = weights(:,:,:,1:min(size(X,4),size(weights,4)),1:min(size(X,5),size(weights,5))); + + % Perform the linear operation and permute back. + X = pagemtimes(weights,X); + X = permute(X,[3,4,5,1,2]); + end + + % Zero out the unrequired frequencies + Xout = zeros([N1,N2,N3,this.OutputSize,size(X,5)],like=X); + Xout(xFreq,yFreq,zFreq,:,:) = X; + + % Make Xout conjugate symmetric. + % Xout(1,:,:,:,:) has the 2d conjugate symmetry + [xPos,xNeg] = iPositiveAndNegativeFrequencies(N1); + [yPos,yNeg] = iPositiveAndNegativeFrequencies(N2); + [zPos,zNeg] = iPositiveAndNegativeFrequencies(N3); + + Xout(1,1,zNeg,:,:) = ... + conj(Xout(1,1,zPos,:,:)); + Xout(1,yNeg,1,:,:) = ... + conj(Xout(1,yPos,1,:,:)); + Xout(1,yNeg, zNeg,:,:) = ... + conj(Xout(1,yPos,zPos,:,:)); + Xout(1,yNeg, zPos,:,:) = ... + conj(Xout(1,yPos,zNeg,:,:)); + Xout(xNeg,1,1,:,:) = ... + conj(Xout(xPos,1,1,:,:)); + + % Xout(:,1,:,:,:) also has 2d conjugate symmetry + Xout(xNeg,1,zNeg,:,:) = ... + conj(Xout(xPos,1,zPos,:,:)); + Xout(xNeg,1,zPos,:,:) = ... + conj(Xout(xPos,1,zNeg,:,:)); + + % Xout(:,:,1,:,:) has the 2d conjugate symmetry + Xout(xNeg,yNeg,1,:,:) = ... + conj(Xout(xPos,yPos,1,:,:)); + Xout(xNeg,yPos,1,:,:) = ... + conj(Xout(xPos,yNeg,1,:,:)); + + % Xout(2:end,:,:,:,:) has 3d conjugate symmetry + Xout(xNeg,yNeg,zNeg,:,:) = ... + conj(Xout(xPos,yPos,zPos,:,:)); + Xout(xNeg,yPos,zNeg,:,:) = ... + conj(Xout(xPos,yNeg,zPos,:,:)); + Xout(xNeg,yNeg,zPos,:,:) = ... + conj(Xout(xPos,yPos,zNeg,:,:)); + Xout(xNeg,yPos,zPos,:,:) = ... + conj(Xout(xPos,yNeg,zNeg,:,:)); + + Xout = ifft(Xout,[],3); + Xout = ifft(Xout,[],2); + Xout = ifft(Xout,[],1,'symmetric'); + + % Reshape back to original size + Xout = reshape(Xout,[N1, N2, N3, this.OutputSize,batchSize]); + + if this.UseBias + Xout = Xout + this.Bias; + end + + Xout = dlarray(Xout,d); + Xout = real(Xout); + end + end +end + diff --git a/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/tfno3d.m b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/tfno3d.m new file mode 100644 index 0000000..e208064 --- /dev/null +++ b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/tfno3d.m @@ -0,0 +1,78 @@ +function net = tfno3d(numModes, latentChannelSize, args) +%TFNO3D - Create a 3D Fourier Neural Operator (FNO) network. +% net = TFNO3D(numModes, latentChannelSize) creates a 3D FNO network with +% the specified number of Fourier modes and latent channel size. +% +% Supported Name-Value pairs are: +% "NumBlocks" Number of FNO blocks (default: 1) +% "ExpandNet" Whether to expand network layers (default: true) +% "LiftingChannelRatio" Ratio for lifting channels (default: 2) +% "ProjectionChannelRatio" Ratio for projection channels (default: 2) +% "InChannels" Number of input channels (default: 1) +% "OutChannels" Number of output channels (default: 1) +% "SpatialLimits" Spatial domain limits [min1, max1; min2, max2; min3, max3] (default: [0, 1; 0, 1; 0, 1]) +% "SpectralRank" Rank for spectral convolution (0-1) (default: 1) +% "LinearFNOSkip" Enable linear skip connection (default: true) +% "ChannelMLPSkip" Enable channel MLP skip connection (default: true) +% "UseSpectralConvBias" Use bias in spectral convolution (default: true) +% "FuseSpectralConv" Directly contract spectral activations with tensorized weights (default: true) +% +% The network architecture consists of: +% 1. Input layer with spatial embedding +% 2. Lifting layers to project input to latent space +% 3. FNO blocks for spectral processing +% 4. Projection layers to map back to output space +% +% Example: +% net = tfno1d([16, 16, 16], 32, NumBlocks=4, InChannels=2, OutChannels=1); + +% Copyright 2026 The MathWorks, Inc. + + arguments + numModes (1, 3) double {mustBePositive, mustBeInteger, mustBeFinite} = [16, 16, 16] + latentChannelSize (1, 1) {mustBePositive, mustBeInteger, mustBeFinite} = 16 + args.NumBlocks (1, 1) double {mustBePositive, mustBeInteger, mustBeFinite} = 2 + args.ExpandNet (1, 1) logical = true + args.LiftingChannelRatio (1, 1) double = 2 + args.ProjectionChannelRatio (1, 1) double = 2 + args.InChannels (1, 1) double = 1 + args.OutChannels (1, 1) double = 1 + args.SpatialLimits (3, 2) double = [0 1; 0 1; 0 1] + args.SpectralRank (1, 1) double {mustBeInRange(args.SpectralRank, 0, 1)} = 1 + args.LinearFNOSkip (1, 1) logical = true + args.ChannelMLPSkip (1, 1) logical = true + args.UseSpectralConvBias (1, 1) logical = true + args.FuseSpectralConv (1, 1) logical = true + end + + liftingChannels = args.LiftingChannelRatio * latentChannelSize; + + layers = [inputLayer([NaN latentChannelSize latentChannelSize latentChannelSize args.InChannels],"BSSSC", Name="input"), ... + spatialEmbeddingLayer3D(args.SpatialLimits, Name="positionEmbdding"), ... + depthConcatenationLayer(2, Name="concat"), ... + convolution3dLayer(1, liftingChannels, Name="lifting1"), ... + convolution3dLayer(1, latentChannelSize, Name="lifting2")]; + + for i = 1:args.NumBlocks + layers(end+1) = fnoBlock3D(numModes, latentChannelSize, ... + Rank=args.SpectralRank, ... + LinearFNOSkip=args.LinearFNOSkip, ... + ChannelMLPSkip=args.ChannelMLPSkip, ... + UseSpectralConvBias=args.UseSpectralConvBias, ... + FuseSpectralConv=args.FuseSpectralConv); + end + + projectionChannels = args.ProjectionChannelRatio * latentChannelSize; + + layers = [layers, ... + convolution3dLayer(1, projectionChannels, Name="proj1"), ... + geluLayer(Name="gelu1"), ... + convolution3dLayer(1, args.OutChannels, Name="proj2")]; + net = dlnetwork; + net = addLayers(net, layers); + net = connectLayers(net,"input","concat/in2"); + + if args.ExpandNet + net = expandLayers(net); + end +end \ No newline at end of file diff --git a/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/tuckerContract.m b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/tuckerContract.m new file mode 100644 index 0000000..b14fcf7 --- /dev/null +++ b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/tuckerContract.m @@ -0,0 +1,92 @@ +function Y = tuckerContract(X, core, factors) +%TUCKERCONTRACT Apply a Tucker-factorized spectral convolution using precontracted spatial modes. +% Y = TUCKERCONTRACT(X, core, factors) computes the output of a single +% Fourier Neural Operator (FNO) spectral convolution layer where the +% convolution kernel is represented in a Tucker factorization. +% +% This implementation performs the contraction +% +% Y = U_out · ( core ×₃ U₁ ×₄ U₂ × ... ×_{d+2} U_d ) · U_inᵀ · X +% +% without explicitly forming the full spectral weight tensor. Spatial +% factors are first contracted into the core, then pagewise RC×RC +% multiplications are used to efficiently apply the operator at each +% spatial location and batch element. +% +% Input and factor layouts: +% X – Input tensor of size [C, B, S₁, ..., S_d], where +% C is the number of channels, +% B is the batch size, +% S₁..S_d are the spatial sizes. +% +% core – Tucker core of size [RC, RC, R₁, ..., R_d], ordered as +% (rOut, rIn, r₁, ..., r_d). +% +% factors – Cell array {U_out, U_in, U₁, ..., U_d} where: +% U_out : [C × RC] +% U_in : [C × RC] +% U_k : [S_k × R_k] for k = 1..d +% +% tuckerContract performs the following steps: +% 1) Compress input channels using U_in.' * X. +% 2) Contract each spatial rank mode of the core with the +% corresponding spatial factor U_k. +% 3) Perform pagewise RC×RC multiplications between the contracted +% core and the compressed input at every (batch, spatial) index. +% 4) Expand rank outputs back to channel space using U_out.' +% +% This function is optimized for minimal intermediate memory use and +% avoids constructing large intermediate tensors. + +% Copyright 2026 The MathWorks, Inc. + + arguments + X + core + factors (1, :) cell + end + + Uout = factors{1}; + UIn = factors{2}; + USpatial = factors(3:end); + numSpatialDims = numel(USpatial); + B = size(X, 2); + RC = size(core, 1); + + % Spatial sizes [S1..Sd] from the first dim of Uk + spatialSizes = cellfun(@(x) size(x, 1), USpatial); + + % Account for X being smaller than core/factors + spatialSizes = min(spatialSizes, size(X, 3:numSpatialDims+2)); + + % 1) Compress channels: X_I = UIn' * X + X = pagemtimes(UIn.', X); % [RC, B, S1..Sd] + + % 2) Pre-contract spatial ranks into the core (once per call) + % For each spatial mode k, left-multiply core's rank mode r_k by Uk (size [Sk x Rk]). + % core: [RC, RC, R1, ..., Rd] → after all k: [RC, RC, S1, ..., Sd] + for k = 1:numSpatialDims + mode = 2 + k; + % Account for X being smaller than the factor or core + factor = USpatial{k}(1:spatialSizes(k), :); + core = contractFactor(core, factor, mode); + end + + % 3) Pagewise rank mixing (vectorized over batch and all spatial locations) + % Form pages so that each page holds one (b, s1..sd) location: + % core pages: [RC, RC, 1, S1, ..., Sd] (same per batch; broadcast on batch) + % X pages: [RC, 1, B, S1, ..., Sd] + % Compute Z_pages = core * X for all pages: + % Z_pages: [RC, 1, B, S1, ..., Sd] → squeeze/permute → Z: [B, S1, ..., Sd, RC] + X = reshape(X, [RC, 1, B, spatialSizes]); % [RC, 1, B, S1..Sd] + core = reshape(core, [RC, RC, 1, spatialSizes]); % [RC, RC, 1, S1..Sd] + + Z = pagemtimes(core, X); % [RC, 1, B, S1..Sd] + Z = permute(squeeze(Z), [2:ndims(Z)-1, 1]); % [B, S1..Sd, RC] + + % 4) Expand to channels: Y_b = Z * Uout' -> [B, S1..Sd, C] + Y = reshape(reshape(Z, [], RC) * Uout.', [B, spatialSizes, size(Uout,1)]); + + % Set output as [S1..Sd, C, B] + Y = permute(Y, [2:(numSpatialDims+2), 1]); % [S1..Sd, C, B] +end \ No newline at end of file diff --git a/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/validation/assertInputHasChannelDim.m b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/validation/assertInputHasChannelDim.m new file mode 100644 index 0000000..d471da3 --- /dev/null +++ b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/validation/assertInputHasChannelDim.m @@ -0,0 +1,6 @@ +function assertInputHasChannelDim(N, numChannels) + if isempty(numChannels) + error("depthwiseConv" + N + "dLayer must have exactly 1 channel dimension"); + end +end + diff --git a/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/validation/assertValidNumConvolutionDimensions.m b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/validation/assertValidNumConvolutionDimensions.m new file mode 100644 index 0000000..fa9b353 --- /dev/null +++ b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/tfno/validation/assertValidNumConvolutionDimensions.m @@ -0,0 +1,6 @@ +function assertValidNumConvolutionDimensions(N, hasTimeDimension, numSpatialDimensions) + if ~(hasTimeDimension + numSpatialDimensions == N) + error("The total number of spatial and time dimensions in " + ... + "depthwiseConv" + N + "dLayer must be " + N + "."); + end +end diff --git a/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/trainingPartitions.m b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/trainingPartitions.m new file mode 100644 index 0000000..91203d6 --- /dev/null +++ b/tensorized-fourier-neural-operator-for-battery-module-cooling-analysis/trainingPartitions.m @@ -0,0 +1,47 @@ +function varargout = trainingPartitions(numObservations,splits) +%TRAININGPARTITONS Random indices for splitting training data +% [idx1,...,idxN] = trainingPartitions(numObservations,splits) returns +% random vectors of indices to help split a data set with the specified +% number of observations, where SPLITS is a vector of length N of +% partition sizes that sum to one. +% +% % Example: Get indices for 50%-50% training-test split of 500 +% % observations. +% [idxTrain,idxTest] = trainingPartitions(500,[0.5 0.5]) +% +% % Example: Get indices for 80%-10%-10% training, validation, test split +% % of 500 observations. +% [idxTrain,idxValidation,idxTest] = trainingPartitions(500,[0.8 0.1 0.1]) + +arguments + numObservations (1,1) double {mustBePositive} + splits (1, :) double {mustBePositive,mustSumToOne} +end + +numPartitions = numel(splits); +varargout = cell(1,numPartitions); + +idx = randperm(numObservations); + +idxEnd = 0; + +for i = 1:numPartitions-1 + idxStart = idxEnd + 1; + idxEnd = idxStart + floor(splits(i)*numObservations) - 1; + + varargout{i} = idx(idxStart:idxEnd); +end + +% Last partition. +varargout{end} = idx(idxEnd+1:end); + +end + +function mustSumToOne(v) +% Validate that value sums to one. + +if sum(v,"all") ~= 1 + error("Value must sum to one.") +end + +end