Manifold Learning for Scientific Applications with SciML Interface.
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Updated
Oct 24, 2022 - Julia
Manifold Learning for Scientific Applications with SciML Interface.
Implementation of Fourier Neural Operator from scratch
[ICLR24] A boundary-embedded neural operator that incorporates complex boundary shape and inhomogeneous boundary values
Official implementation of Operator-ProbConserv: OOD UQ for Neural Operators
Rheology-informed Machine Learning Projects
Learning function operators with neural networks.
Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."
PDEBench: An Extensive Benchmark for Scientific Machine Learning
Physics-Informed Neural networks for Advanced modeling
Learning in infinite dimension with neural operators.
This repository is the official implementation of the paper Convolutional Neural Operators for robust and accurate learning of PDEs
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