A library for scientific machine learning and physics-informed learning
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Updated
Jun 24, 2024 - Python
A library for scientific machine learning and physics-informed learning
Generative Pre-Trained Physics-Informed Neural Networks Implementation
Official imprementation of the paper "A general deep learning method for computing molecular parameters of viscoelastic constitutive model by solving an inverse problem"
NVFi in PyTorch (NeurIPS 2023)
Source code for Zero-Shot Wireless Indoor Navigation through Physics-Informed Reinforcement Learning
Physics-informed deep super-resolution of spatiotemporal data
Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.
Code for the NeurIPS 2021 paper "Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features"
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
Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs
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.
Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]
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