A library for scientific machine learning and physics-informed learning
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Updated
May 7, 2024 - Python
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]
Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing
physics-informed neural network for elastodynamics problem
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
The SciML Scientific Machine Learning Software Organization Website
Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.
Generative Pre-Trained Physics-Informed Neural Networks Implementation
NVFi in PyTorch (NeurIPS 2023)
Physics-informed deep super-resolution of spatiotemporal data
A curated list of awesome Scientific Machine Learning (SciML) papers, resources and software
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
Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs
A C++ library for physics-informed spatial and functional data analysis over complex domains.
Using TensorFlow for physics-informed neural networks for scientific machine learning (SciML)
Nonnegative Matrix Factorization + k-means clustering and physics constraints for Unsupervised and Physics-Informed Machine Learning
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