Learning in infinite dimension with neural operators.

PDEBench: An Extensive Benchmark for Scientific Machine Learning

Physics-Informed Neural networks for Advanced modeling

A Library for Advanced Neural PDE Solvers.

This repository is the official implementation of the paper Convolutional Neural Operators for robust and accurate learning of PDEs

Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."

Learning function operators with neural networks.

[ICLR24] A boundary-embedded neural operator that incorporates complex boundary shape and inhomogeneous boundary values

Rheology-informed Machine Learning Projects

Official implementation of Operator-ProbConserv: OOD UQ for Neural Operators

Implementation of Fourier Neural Operator from scratch

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

LUNO: Linearized Predictive Uncertainty in Neural Operators

前沿物理仿真与智能感知技术调研资料库 | Frontier physics simulation research notes

Final projects for 401-4656-21L AI in Sciences and Engineering @ ETHz. Includes implementation of Fourier Neural Operator (FNO) with time dependency, data-driven symbolic regression with PDE-Find and foundation model based on FNO for phase-field dynamics

Physics-informed Fourier Neural Operator (FNO) framework for fast pricing and Greeks computation of barrier options under Black–Scholes PDE, developed for an MSc thesis.

Physics-Enhanced Machine Learning

A comparative analysis of DeepONet and FNO architectures, benchmarking their performance on Function-to-Function (Heat Equation) vs. Parameter-to-Function (Elastic Bar) PDE problems to motivate hybrid operator designs.

Frontier physics simulation survey — Neural operators, GNN simulators, diffusion models, physics engines, world models