Convolutional autoencoders, clustering, and POD for low-dimensional parametrization of flow equations

DOI10.1016/j.camwa.2024.08.032MaRDI QIDQ6663360

Yong-Ho Kim, Jan Heiland

Publication date: 14 January 2025

Published in: Computers & Mathematics with Applications (Search for Journal in Brave)

zbMATH Keywords

clusteringmodel order reductionlinear parameter varying (LPV) systemsconvolutional autoencoders

Mathematics Subject Classification ID

Fluid mechanics (76-XX) Partial differential equations (35-XX)

Cites Work

• Title not available (Why is that?)
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• ConvPDE-UQ: convolutional neural networks with quantified uncertainty for heterogeneous elliptic partial differential equations on varied domains
• Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
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• Finite element decomposition and minimal extension for flow equations
• Low-order model for successive bifurcations of the fluidic pinball
• Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions

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