AISE

Learning in infinite dimension with neural operators.

IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.

Creating animation sequences between sparse key frames using motion phase features.

This repository is the official implementation of the paper Convolutional Neural Operators for robust and accurate learning of PDEs

Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."

Learning function operators with neural networks.

[ICLR24] A boundary-embedded neural operator that incorporates complex boundary shape and inhomogeneous boundary values

Official repo for separable operator networks -- extreme-scale operator learning for parametric PDEs.

PyTorch implemention of the Position-induced Transformer for operator learning in partial differential equations

Rheology-informed Machine Learning Projects