Grid-based approximation of partial differential equations in Julia

Python package for solving partial differential equations using finite differences.

Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]

Code for the paper "Poseidon: Efficient Foundation Models for PDEs"

Geophysical fluid dynamics pseudospectral solvers with Julia and FourierFlows.jl.

Start solving PDEs in Julia with Gridap.jl

Generative Pre-Trained Physics-Informed Neural Networks Implementation

NRPy+, BlackHoles@Home, SENRv2, and the NRPy+ Jupyter Tutorial: Python-Based Code Generation for Numerical Relativity... and Beyond!

Rensselaer's Optimistic Simulation System

[NeurIPS 2025] Geometry Aware Operator Transformer As An Efficient And Accurate Neural Surrogate For PDEs On Arbitrary Domains

A Python library for solving any system of hyperbolic or parabolic Partial Differential Equations. The PDEs can have stiff source terms and non-conservative components.

High-performance finite element toolbox in Julia

Three Dimensional Magnetohydrodynamic(MHD) pseudospectral solvers written in julia with FourierFlows.jl

Spatial bio-chemical reaction model editor and simulator

Solver for 1D nonlinear partial differential equations in Julia based on the collocation method of Skeel and Berzins and providing an API similar to MATLAB's pdepe

Physics-informed neural networks (PINNs)

Folax (Finite Operator Learning with JAX) is a framework for solving and optimizing PDEs by integrating machine learning with numerical methods in computational mechanics.

Simian Process Oriented Conservative JIT PDES from LANL

Repository with notebooks about Physics Informed Neural Networks, written in JAX + Flax.

C++/Python implementation of the NNsPOD method based on ITHACA-FV and OpenFOAM

A 3D solver based on two coupled PDEs for calculation of glaze and rime icing on aircraft surface

Elucidating the Design Choice of Probability Paths in Flow Matching for Forecasting

Source code implementation for the paper "Handling geometrical variability in nonlinear reduced order modeling through Continuous Geometry-Aware DL-ROMs"

Materials for for SIF3012 Computational Physics course. This course is designed for Physics students taking Computational Physics course. Some materials need to be guided through lectures series (provided in Spectrum and class)

Neuromorphic event-driven simulator in C and MPI (successor of NeMo https://github.com/markplagge/NeMo)

A Parallel Discrete Event Simulation Engine with Examples

Exercise solutions of the course : Introduction to Computational Physics offered at ETH Zurich

Stable High-Order Cut-Cell Solver

PSML: parallel system modeling and simulation language for electronic system level

Scalable Markov chain Monte Carlo Sampling Methods for Large-scale Bayesian Inverse Problems Governed by PDEs