A library for scientific machine learning and physics-informed learning

Learning in infinite dimension with neural operators.

An acausal 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

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

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

Simple finite element assemblers

Physics-Informed Neural networks for Advanced modeling

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

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

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

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

Generalized and Personalized

Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R

A framework for hydrodynamics explorations and prototyping

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

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

DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia

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

TCAD Semiconductor Device Simulator