A library for scientific machine learning and physics-informed learning
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Updated
May 28, 2022 - Python
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A library for scientific machine learning and physics-informed learning
Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem
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
The SciML Scientific Machine Learning Software Organization Website
Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]
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)
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)
Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs
Weak For Generalized Hamiltonian Learning
Physics-informed deep super-resolution of spatiotemporal data
Nonnegative Matrix Factorization + k-means clustering and physics constraints for Unsupervised and Physics-Informed Machine Learning
Smart Tensors Tutorials
Deep Latent Force Models
Nonnegative Tensor Factorization + k-means clustering and physics constraints for Unsupervised and Physics-Informed Machine Learning
Physics-informed refinement learning for equation discovery
Physics-based machine learning with dynamic Boltzmann distributions
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https://arxiv.org/abs/2012.06684
@samuela is there code to share that could become a tutorial? I think it would be good to make one.