A repository for the discussion of PDE tooling for scientific machine learning (SciML) and physics-informed machine learning
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Updated
Nov 21, 2019 - Jupyter Notebook
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)
Accompanying code for "Weak form generalized Hamiltonian learning"
Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]
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.
Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs
Physics-based machine learning with dynamic Boltzmann distributions
physics-informed neural network for elastodynamics problem
Physics-informed refinement learning for equation discovery
Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing
Code for the NeurIPS 2021 paper "Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features"
This repo contains the code for solving Poisson Equation using Physics Informed Neural Networks
study code for physics informed machine learning and deep learning
Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.
Data-parallel PINNs with Horovod
Physics-informed deep super-resolution of spatiotemporal data
The implementation of the paper "A Machine Learning Pressure Emulator for Hydrogen Embrittlement", accepted to ICML 2023 SynS & ML Workshop
Source code for Zero-Shot Wireless Indoor Navigation through Physics-Informed Reinforcement Learning
Navier-Stokes oil dynamics in a rectangular 3D tank, physics-informed neural network approach
This repository is the implementation of the paper "A Variational Autoencoder Framework for Robust, Physics-Informed Cyberattack Recognition in Industrial Cyber-Physical Systems"
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