Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.

Bayesian inference on spatial and spatiotemporal data, faster than you can say "Cholesky!"

Code and data from the "Spatial and Spatio-temporal Bayesian Models with R-INLA" book

Stochastic PDE solvers (SPDE) built on top of exponax: Exponential Euler-Maruyama stepper for the stochastic Allen-Cahn equation with additive/multiplicative Q-Wiener noise, tamed nonlinearities, ensemble utilities, Richardson extrapolation, and a Strang-split hybrid SSA scaffold.

Large-scale prior fields based on the SPDE approach to Matérn fields

An introductory course to the INLA-software

Applied Spatial Data Analysis in geoscience

Large-Scale Prior Fields via SPDEs

Elevational variation in tropical trees analysis

Bibliograph bank for stochastic PDE and related fields

Julia scripts associated with "A White Noise Approach to Evolutionary Ecology" by Week et. al. (2020) for simulating branching Brownian motions and other measure-valued branching processes, approximating their diffusion limits, solving associated SPDE and solving SDE corresponding to an eco-evolutionary model of a competitive community.

Compare two codebases quickly in the browser, showing added, deleted, modified, and moved files with a fast, non-blocking diff engine.

Code for proceeding paper in FIMH2019