Topic: physics-informed-learning Goto Github
Some thing interesting about physics-informed-learning
Some thing interesting about physics-informed-learning
physics-informed-learning,Navier-Stokes oil dynamics in a rectangular 3D tank, physics-informed neural network approach
User: axik0
physics-informed-learning,Accompanying code for "Weak form generalized Hamiltonian learning"
User: coursekevin
physics-informed-learning,A C++ library for physics-informed spatial and functional data analysis over complex domains.
Organization: fdapde
Home Page: https://fdapde.github.io/
physics-informed-learning,Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants. To appear in the Proceedings of the Royal Society A.
User: gaoliyao
physics-informed-learning,Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems
User: jbramburger
physics-informed-learning,Going through the tutorial on Physics-informed Neural Networks: https://github.com/madagra/basic-pinn
User: jbris
Home Page: https://jbris.github.io/physics_informed_nn_tutorial/
physics-informed-learning,Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.
User: katiana22
physics-informed-learning,Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]
User: levimcclenny
Home Page: https://arxiv.org/abs/2009.04544
physics-informed-learning,A library for scientific machine learning and physics-informed learning
User: lululxvi
Home Page: https://deepxde.readthedocs.io
physics-informed-learning,A curated list of awesome Scientific Machine Learning (SciML) papers, resources and software
User: martinuzzifrancesco
physics-informed-learning,PINEURODEs is a repository collecting CMS group research work on the application of neural (stochastic/ordinary) differential equations and physically-informed neural networks to model complex multiscale systems.
User: migduroli
Home Page: https://www.imperial.ac.uk/complex-multiscale-systems/
physics-informed-learning,The implementation of the paper "A Machine Learning Pressure Emulator for Hydrogen Embrittlement", accepted to ICML 2023 SynS & ML Workshop
User: minhtriet
Home Page: https://syns-ml.github.io/2023/assets/papers/16.pdf
physics-informed-learning,This repository is the implementation of the paper "A Variational Autoencoder Framework for Robust, Physics-Informed Cyberattack Recognition in Industrial Cyber-Physical Systems"
User: navidaftabi
Home Page: https://arxiv.org/abs/2310.06948
physics-informed-learning,Source code for Zero-Shot Wireless Indoor Navigation through Physics-Informed Reinforcement Learning
User: panshark
physics-informed-learning,Physics-informed refinement learning for equation discovery
User: paulpuren
physics-informed-learning,Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs
User: paulpuren
physics-informed-learning,Physics-informed deep super-resolution of spatiotemporal data
User: paulpuren
physics-informed-learning,Data-parallel PINNs with Horovod
User: pescap
physics-informed-learning,Uncertainty-penalized Bayesian information criterion (UBIC) for PDE Discovery
User: pongpisit-thanasutives
Home Page: https://ieeexplore.ieee.org/document/10401233
physics-informed-learning,Uncertainty-penalized Bayesian information criterion (UBIC) for PDE Discovery
User: pongpisit-thanasutives
Home Page: https://ieeexplore.ieee.org/document/10401233
physics-informed-learning,Uncertainty-penalized Bayesian information criterion (UBIC) for PDE Discovery
User: pongpisit-thanasutives
Home Page: https://ieeexplore.ieee.org/document/10401233
physics-informed-learning,Rheology-informed Machine Learning Projects
Organization: procf
physics-informed-learning,physics-informed neural network for elastodynamics problem
User: raocp
physics-informed-learning,Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem
Organization: sciml
Home Page: https://docs.sciml.ai/DiffEqDocs/stable/
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
Organization: sciml
Home Page: https://docs.sciml.ai/DiffEqFlux/stable
physics-informed-learning,A repository for the discussion of PDE tooling for scientific machine learning (SciML) and physics-informed machine learning
Organization: sciml
Home Page: https://tutorials.sciml.ai/
physics-informed-learning,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)
Organization: sciml
Home Page: https://docs.sciml.ai/QuasiMonteCarlo/stable/
physics-informed-learning,The SciML Scientific Machine Learning Software Organization Website
Organization: sciml
Home Page: https://sciml.ai/
physics-informed-learning,Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
Organization: sciml
Home Page: https://docs.sciml.ai/SciMLTutorialsOutput/stable/
physics-informed-learning,Using TensorFlow for physics-informed neural networks for scientific machine learning (SciML)
Organization: sciml
Home Page: https://sciml.ai/
physics-informed-learning,Generative Pre-Trained Physics-Informed Neural Networks Implementation
User: skoohy
physics-informed-learning,This repo contains the code for solving Poisson Equation using Physics Informed Neural Networks
User: sm823zw
physics-informed-learning,Nonnegative Matrix Factorization + k-means clustering and physics constraints for Unsupervised and Physics-Informed Machine Learning
Organization: smarttensors
Home Page: https://smarttensors.github.io
physics-informed-learning,Nonnegative Tensor Factorization + k-means clustering and physics constraints for Unsupervised and Physics-Informed Machine Learning
Organization: smarttensors
Home Page: https://smarttensors.github.io
physics-informed-learning,Smart Tensors Tutorials
Organization: smarttensors
physics-informed-learning,Physics-based machine learning with dynamic Boltzmann distributions
User: smrfeld
Home Page: https://smrfeld.github.io/phys_dbd
physics-informed-learning,Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing
Organization: tensordiffeq
Home Page: http://docs.tensordiffeq.io
physics-informed-learning,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.
User: terencetaothucb
physics-informed-learning,Code for the NeurIPS 2021 paper "Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features"
User: tomcdonald
physics-informed-learning,NVFi in PyTorch (NeurIPS 2023)
Organization: vlar-group
physics-informed-learning,Official imprementation of the paper "A general deep learning method for computing molecular parameters of viscoelastic constitutive model by solving an inverse problem"
User: yeminghui
physics-informed-learning,study code for physics informed machine learning and deep learning
User: zinzinbin
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