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]

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

Start solving PDEs in Julia with Gridap.jl

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

Rensselaer's Optimistic Simulation System

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

Generative Pre-Trained Physics-Informed Neural Networks Implementation

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.

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

FEM toolbox with automatic code generation, HPC support, and more.

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

Spatial bio-chemical reaction model editor and simulator

Simian Process Oriented Conservative JIT PDES from LANL

Physics-informed neural networks (PINNs)

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

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)

Deep-learning model for optimised proper orthogonal decomposition of non-linear, hyperbolic, parametric PDEs based on a pre-processing method of the full-order solutions

Stable High-Order Cut-Cell Solver