The first global synthetic dataset for physics-ML seismic wavefield modeling and full-waveform inversion
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Updated
Jun 24, 2024 - Python
The first global synthetic dataset for physics-ML seismic wavefield modeling and full-waveform inversion
Code for ICML 24 paper "Implicit Representations via Operator Learning"
Using FNO to learning elasticity model of composite materials
Submission to the Stanford FLAME AI 2023 - ML Challenge
Positron's Milky Way Energy Loss using Operator learning
Implementation of neural operator papers in PyTorch for easier usage. Achieve SOTA in PDE prediction.
This repository contains the code for the paper: Deciphering and integrating invariants for neural operator learning with various physical mechanisms, National Science Review, 2024
Neural Operators implemented with JAX and Equinox
Neural Operator-Assisted Computational Fluid Dynamics in PyTorch
Code for the paper ``Error Bounds for Learning with Vector-Valued Random Features''
Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning
Code for the paper "The Random Feature Model for Input-Output Maps between Banach Spaces"
An extension of Fourier Neural Operator to finite-dimensional input and/or output spaces.
Neural Operators with Applications to the Helmholtz Equation
Automatic Functional Differentiation in JAX
This repository contains the code for the paper: Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation
Official implementation of Scalable Transformer for PDE surrogate modelling
Official implementation of the NeurIPS 23 spotlight paper of ♾️InfGCN♾️.
Codomain attention neural operator for single to multi-physics PDE adaptation.
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