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
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