48 Open Source Pde Software Projects

Source code for the Processing Core and Development Environment (PDE)

Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components

A modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

A library for scientific machine learning

Source code for APDE: Create and run Processing sketches on an Android device.

Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

A framework for hydrodynamics explorations and prototyping

Python package for numerical derivatives and partial differential equations in any number of dimensions.

An adaptive mesh, astrophysical compressible (radiation-, magneto-) hydrodynamics simulation code for massively parallel CPU and GPU architectures.

R Language Mode in Processing for Creative Coding, created by @gaocegege, maintained by @jeremydouglass

Linear operators for discretizations of differential equations and scientific machine learning (SciML)

FEATool Multiphysics - "Physics Simulation Made Easy" (Fully Integrated FEA, FEniCS, OpenFOAM, SU2 Solver GUI & Simulation Platform)

a collection of small and standalone utilities for image processing, written in C

BOUT++: Plasma fluid finite-difference simulation code in curvilinear coordinate systems

Benchmarks for scientific machine learning (SciML) software and differential equation solvers

The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems

A Julia package to perform Bifurcation Analysis

TCAD Semiconductor Device Simulator

Simple one-dimensional examples of various hydrodynamics techniques

Solve forward-looking PDEs (e.g. HJB equations).

SG⁺⁺ – the numerical library for Sparse Grids in all their variants.

A scientific machine learning (SciML) wrapper for the FEniCS Finite Element library in the Julia programming language

An adaptive mesh hydrodynamics simulation code for low Mach number reacting flows

Sparse Grid Discretization with the Discontinuous Galerkin Method for solving PDEs

Finite volume discretization tools for Python.

A low Mach number stellar hydrodynamics code

FuturEye_JIT is a Java Finite Element Method (FEM) library for solving PDE based forward/inverse problems as efficient as hand written FORTRAN code.

A simple finite volume tool for Julia

Adaptive Finite Element Package

Numerical Experiments

PyFRAP: A Python based FRAP analysis tool box

BoSSS is a flexible framework for the development, evaluation & application of numerical discretization schemes based on the Discontinuous Galerkin method

MFM community development code

Simple finite element assemblers

Universal modeling and simulation of fluid dynamics upon machine learning

Deep learning library for solving differential equations on top of PyTorch.

learning the solution operator for partial differential equations in pure Julia.

Operator Inference for data-driven, non-intrusive model reduction of dynamical systems.

Visualize curve shortening flow in your browser.

Pytorch code for learning an underlying PDE from given data.

Collection of Processing source code

ETH course - Solving PDEs in parallel on GPUs

Leibniz is a python package which provide facilities to express learnable partial differential equations with PyTorch

PDE-VAE: Variational Autoencoder for Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning

Mimetic Operators Library Enhanced

A JAX-based research framework for writing differentiable numerical simulators with arbitrary discretizations

Slides/notes and Jupyter notebook demos for an introductory course of numerical methods for PDEs