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

Official Implementation of ProbHardE2E: End-to-End Probabilistic Framework for Learning with Hard Constraints

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

Implementation of Fourier Neural Operator from scratch

LUNO: Linearized Predictive Uncertainty in Neural Operators

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.

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.

Physics-Enhanced Machine Learning

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

Physics-Informed Machine Learning for Precision UAV Control: Adaptive Transformers with Safety Guarantees (EAAI 2026)