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.
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
Generalized and Personalized
A Julia package to perform Bifurcation Analysis
Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
Castro (Compressible Astrophysics): An adaptive mesh, astrophysical compressible (radiation-, magneto-) hydrodynamics simulation code for massively parallel CPU and GPU architectures.
A framework for hydrodynamics explorations and prototyping
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
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