Learning in infinite dimension with neural operators.
翻译 - 使用傅立叶变换学习微分方程中的算子。
#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.
ICML2024: Equivariant Graph Neural Operator for Modeling 3D Dynamics
Efficient, Accurate, and Streamlined Training of Physics-Informed Neural Networks
Datasets and code for results presented in the BOON paper
Official implementation of Scalable Transformer for PDE surrogate modelling
[ICLR 2025] Neural Operator-Assisted Computational Fluid Dynamics in PyTorch
#计算机科学#A multiphase multiphysics dataset and benchmarks for scientific machine learning
An extension of Fourier Neural Operator to finite-dimensional input and/or output spaces.
This repository contains the code for the paper: Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation (IEEE TPAMI 2025)
#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♾️.
This repository contains the code for the paper: Deciphering and integrating invariants for neural operator learning with various physical mechanisms, National Science Review, 2024
#计算机科学#Official implementation of the paper "Neural Hamilton: Can A.I. Understand Hamiltonian Mechanics?"
Code for the paper "The Random Feature Model for Input-Output Maps between Banach Spaces" (SIREV SIGEST 2024, SISC 2021)