Welcome to the Physics-based Deep Learning Book v0.3 - the GenAI Edition
A Physics-Informed Neural Network (PINN) framework for solving partial differential equations (PDEs) with FastAPI integration. This project implements PINNs for various physical systems including simple harmonic motion, heat transfer, wave propagation, and fluid dynamics. The framework provides a modular architecture for training.
C++ library for solving Hamilton-Jacobi equations and related PDEs using high-order numerical methods
Development and testing of MeshfreeTrixi.jl
Meshfree extension to Trixi using RBF-based numerics
High-performance C++ implementations of iterative methods for solving large sparse linear systems and computing eigenvalues, including Conjugate Gradient, Preconditioned CG, Jacobi, Arnoldi, and QR-based solvers, optimized for memory efficiency and numerical stability.
Quantum solver for nonlinear PDEs using a forward Euler time-stepping scheme.
Kolmogorov–Arnold Networks for PDE solving (Master Thesis)
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