Learning in infinite dimension with neural operators.
PDEBench: An Extensive Benchmark for Scientific Machine Learning
Physics-Informed Neural networks for Advanced modeling
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
Bridging Neural Operators and Numerical Methods
[ICPR 2024] FNOReg: Resolution-Robust Medical Image Registration Method Based on Fourier Neural Operator
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