A library for scientific machine learning and physics-informed learning
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Updated
Jul 11, 2024 - Python
A library for scientific machine learning and physics-informed learning
Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]
physics-informed neural network for elastodynamics problem
Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing
Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.
Generative Pre-Trained Physics-Informed Neural Networks Implementation
NVFi in PyTorch (NeurIPS 2023)
Physics-informed deep super-resolution of spatiotemporal data
Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs
Code for the NeurIPS 2021 paper "Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features"
Source code for Zero-Shot Wireless Indoor Navigation through Physics-Informed Reinforcement Learning
Official imprementation of the paper "A general deep learning method for computing molecular parameters of viscoelastic constitutive model by solving an inverse problem"
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.
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