Positron's Milky Way Energy Loss using Operator learning
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Updated
Mar 19, 2024
Positron's Milky Way Energy Loss using Operator learning
Using FNO to learning elasticity model of composite materials
Exploring fluid dynamics from a deep learning perspective.
Submission to the Stanford FLAME AI 2023 - ML Challenge
Code for the paper ``Error Bounds for Learning with Vector-Valued Random Features''
Implementation of neural operator papers in PyTorch for easier usage. Achieve SOTA in PDE prediction.
Code for ICML 24 paper "Implicit Representations via Operator Learning"
Code for the paper "The Random Feature Model for Input-Output Maps between Banach Spaces"
The first global synthetic dataset for physics-ML seismic wavefield modeling and full-waveform inversion
Neural Operators with Applications to the Helmholtz Equation
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
This repository contains the code for the paper: Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation
An extension of Fourier Neural Operator to finite-dimensional input and/or output spaces.
A multiphase multiphysics dataset and benchmarks for scientific machine learning
Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning
Neural Operator-Assisted Computational Fluid Dynamics in PyTorch
Codomain attention neural operator for single to multi-physics PDE adaptation.
Official implementation of Scalable Transformer for PDE surrogate modelling
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