Nonnegative Matrix Factorization + k-means clustering and physics constraints for Unsupervised and Physics-Informed Machine Learning
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Updated
May 25, 2024 - HTML
Nonnegative Matrix Factorization + k-means clustering and physics constraints for Unsupervised and Physics-Informed Machine Learning
A library for scientific machine learning and physics-informed learning
The SciML Scientific Machine Learning Software Organization Website
Generative Pre-Trained Physics-Informed Neural Networks Implementation
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
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)
Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem
A C++ library for physics-informed spatial and functional data analysis over complex domains.
Uncertainty-penalized Bayesian information criterion (UBIC) for PDE Discovery
Nonnegative Tensor Factorization + k-means clustering and physics constraints for Unsupervised and Physics-Informed Machine Learning
Rheology-informed Machine Learning Projects
PDE discovery using UBIC (uncertainty-penalized Bayesian information criterion)
Uncertainty-penalized Bayesian information criterion (UBIC) for PDE Discovery
Uncertainty-penalized Bayesian information criterion (UBIC) for PDE Discovery
Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems
Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants. To appear in the Proceedings of the Royal Society A.
Official imprementation of the paper "A general deep learning method for computing molecular parameters of viscoelastic constitutive model by solving an inverse problem"
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.
Going through the tutorial on Physics-informed Neural Networks: https://github.com/madagra/basic-pinn
Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
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