Learning in infinite dimension with neural operators.
#计算机科学#A library for Koopman Neural Operator with Pytorch.
#计算机科学#This repository is the official implementation of the paper Convolutional Neural Operators for robust and accurate learning of PDEs
Automatic Functional Differentiation in JAX
Codomain attention neural operator for single to multi-physics PDE adaptation.
Efficient, Accurate, and Streamlined Training of Physics-Informed Neural Networks
ICML2024: Equivariant Graph Neural Operator for Modeling 3D Dynamics
[ICLR 2025] Neural Operator-Assisted Computational Fluid Dynamics in PyTorch
Official implementation of Scalable Transformer for PDE surrogate modelling
Datasets and code for results presented in the BOON paper
[ICLR2025] Wavelet Diffusion Neural Operator (WDNO) uses diffusion models on wavelet space for generative PDE simulation and control.
#计算机科学#A multiphase multiphysics dataset and benchmarks for scientific machine learning
This repository contains the code for the paper: Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation (IEEE TPAMI 2025)
An extension of Fourier Neural Operator to finite-dimensional input and/or output spaces.
#计算机科学#Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning
Official implementation of the NeurIPS 23 spotlight paper of ♾️InfGCN♾️.
The first global synthetic dataset for physics-ML seismic wavefield modeling and full-waveform inversion
#计算机科学#Official implementation of the paper "Neural Hamilton: Can A.I. Understand Hamiltonian Mechanics?"