neural-operators

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

Differentiable numerical solvers and scientific machine learning on one JAX substrate; you write the math, it stays differentiable end to end.

Rheology-informed Machine Learning Projects

Implementations of three neural operators and application in Bayesian inference problems

One-stop equinox model repository

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

[ICML 2026] Unveiling Multi-regime Patterns in SciML: Distinct Failure Modes and Regime-specific Optimization

LUNO: Linearized Predictive Uncertainty in Neural Operators

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

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

ReservoirNeuralBench – A controlled benchmark of neural surrogates for 3D reservoir simulation on the Norne field.

Implementation of Fourier Neural Operator from scratch

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.