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

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.

Code for ENM5310 Final Project

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

Spectral Physics-informed Finite Operator Learning

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

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

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

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

Implementation of Fourier Neural Operator from scratch

Solving multiphysics-based inverse problems with learned surrogates and constraints

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)

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.

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

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

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

Learning in infinite dimension with neural operators.