A library for scientific machine learning and physics-informed learning
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Updated
Jun 5, 2024 - Python
A library for scientific machine learning and physics-informed learning
My personal development environment for Neovim
Scientific Learning project on the monodomain equation
Generalized and Personalized
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
Modern C++20 finite element method and shape optimization framework.
Neural Network Implicit Representation of Partial Differential Equations
A comfortable and functional Neovim PDE with an eye pleasing UI, that aims to be simple to extend and modify.
🔍 finite element analysis for continuum mechanics of solid bodies
Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
Learning in infinite dimension with neural operators.
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
This repository is the official implementation of the paper Convolutional Neural Operators for robust and accurate learning of PDEs
A Julia package to perform Bifurcation Analysis
TCAD Semiconductor Device Simulator
Symbolic calculus for partial differential equations (and variational forms)
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
Castro (Compressible Astrophysics): An adaptive mesh, astrophysical compressible (radiation-, magneto-) hydrodynamics simulation code for massively parallel CPU and GPU architectures.
Torch based framework for PDE solving with Green's functions
Solve Fractional Differential Equations using high performance numerical methods
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