Learning in infinite dimension with neural operators.

DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia

Code for Characterizing Scaling and Transfer Learning Behavior of FNO in SciML

Neural Operator-Assisted Computational Fluid Dynamics in PyTorch

An extension of Fourier Neural Operator to finite-dimensional input and/or output spaces.

A PyTorch implementation of MedSegDiff, a diffusion probabilistic model designed for medical image segmentation.

Code to reproduce the results in "Conditional score-based diffusion models for Bayesian inference in infinite dimensions", NeurIPS 2023

Code for the paper "The Random Feature Model for Input-Output Maps between Banach Spaces" (SIREV SIGEST 2024, SISC 2021)

Solving multiphysics-based inverse problems with learned surrogates and constraints

Implementation of Fourier Neural Operator from scratch

The first GAN-based tabular data synthesizer integrating the Fourier Neural Operator for global dependency imitation

Code for the paper ``Error Bounds for Learning with Vector-Valued Random Features'' (NeurIPS 2023, Spotlight)

[ICPR 2024] FNOReg: Resolution-Robust Medical Image Registration Method Based on Fourier Neural Operator

Fokker Planck based Data Assimilation method using Fourier Neural Operators as integrator

CFNO is a variant of Fourier Neural Operators that uses a Chebychev expansion in the vertical direction.