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

Generative Pre-Trained Physics-Informed Neural Networks Implementation

Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.

Physics-informed deep super-resolution of spatiotemporal data

NVFi in PyTorch (NeurIPS 2023)

Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs

Source code for Zero-Shot Wireless Indoor Navigation through Physics-Informed Reinforcement Learning

Code for the NeurIPS 2021 paper "Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features"

Official Implementation of Integrating Physics-Informed Vectors for Improved Wind Speed Forecasting with Neural Networks

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.

Exploring the concepts of Physics Informed Neural Networks. Coding a Physics Informed Neural Network to simulate a harmonic oscillator and solve a simple first-order ordinary differential equation.