A library for scientific machine learning and physics-informed learning

Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem

Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]

physics-informed neural network for elastodynamics problem

Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing

Lightweight and easy generation of quasi-Monte Carlo sequences with a ton of different methods on one API for easy parameter exploration in scientific machine learning (SciML)

Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems

Generative Pre-Trained Physics-Informed Neural Networks Implementation

Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.

The SciML Scientific Machine Learning Software Organization Website

[IROS 2024] [ICML 2024 Workshop Differentiable Almost Everything] Physics-informed model to predict robot-terrain interactions from RGB images.

A curated list of awesome Scientific Machine Learning (SciML) papers, resources and software

NVFi in PyTorch (NeurIPS 2023)

Physics-informed deep super-resolution of spatiotemporal data

Sunwoda Electronic Co., Ltd, and Tsinghua Berkeley Shenzhen Institute (TBSI) generate the TBSI Sunwoda Battery Dataset. We open-source this dataset to inspire more data-driven novel material verification, battery management research and applications.

Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs

A C++ library for physics-informed spatial and functional data analysis over complex domains.

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

A repository for the discussion of PDE tooling for scientific machine learning (SciML) and physics-informed machine learning