fourier-neural-operator

Learning in infinite dimension with neural operators.

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

[ICLR 2025] Neural Operator-Assisted Computational Fluid Dynamics in PyTorch

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

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

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

Official implementation of the NeurIPS 2025 spotlight paper "Light-Weight Diffusion Multiplier and Uncertainty Quantification for Fourier Neural Operators"

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

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

Solving multiphysics-based inverse problems with learned surrogates and constraints

AdaptFNO: Adaptive Fourier Neural Operator with Dynamic Spectral Modes and Multiscale Learning

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

Spectral Physics-informed Finite Operator Learning

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

Implementation of Fourier Neural Operator from scratch

HG-GFNO: Integrated spatio-temporal traffic forecasting with hybrid graph convolutions and GFNO.

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

Scientific machine learning for JAX/Flax NNX: neural operators (FNO family, DeepONet, PINO, UNO), physics-informed networks (PINN, FBPINN, XPINN), E(3)-equivariant atomistic potentials (SchNet, PaiNN, NequIP), differentiable Kohn-Sham DFT, SINDy equation discovery, uncertainty quantification (conformal, GPs, SBI), PDEBench benchmarking.