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
Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems
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)
Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing
Generative Pre-Trained Physics-Informed Neural Networks Implementation
[IROS 2024] [ICML 2024 Workshop Differentiable Almost Everything] MonoForce: Learnable Image-conditioned Physics Engine
Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.
A curated list of awesome Scientific Machine Learning (SciML) papers, resources and software
The SciML Scientific Machine Learning Software Organization Website
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 deep super-resolution of spatiotemporal data
NVFi in PyTorch (NeurIPS 2023)
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.
Rheology-informed Machine Learning Projects
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