Learning in infinite dimension with neural operators.
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Updated
May 21, 2024 - Python
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
[ICLR24] A boundary-embedded neural operator that incorporates complex boundary shape and inhomogeneous boundary values
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.
Rheology-informed Machine Learning Projects
Official implementation of Operator-ProbConserv: OOD UQ for Neural Operators
Implementation of Fourier Neural Operator from scratch
Manifold Learning for Scientific Applications with SciML Interface.
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