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

NVFi in PyTorch (NeurIPS 2023)

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

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

Using TensorFlow for physics-informed neural networks for scientific machine learning (SciML)