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

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 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

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

Julia package for generating collocation points inside various domains, with support for variable point density. The primary goal here is to use this for solving PDEs with physics-informed machine learning

Spectral Physics-informed Finite Operator Learning

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

These works are under Prof. Akshay Joshi, Mechanical Engineering Dept., IISc Bangalore. On FNOs (Fourier Neural Networks) in multi-dimensions for material property analysis, in different circumstances.

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

Code for ENM5310 Final Project