Polytopic autoencoders with smooth clustering for reduced-order modeling of flows

With the advancement of neural networks, there has been a notable increase, both in terms of quantity and variety, in research publications concerning the application of autoencoders to reduced-order models. We propose a polytopic autoencoder architecture that includes a lightweight nonlinear encode...

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Vydané v:	Journal of computational physics Ročník 521; s. 113526
Hlavní autori:	Heiland, Jan, Kim, Yongho
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Inc 15.01.2025
Predmet:	Clustering Convex polytope Convolutional autoencoders Linear parameter-varying (LPV) systems Model order reduction Polytopic LPV system Convolutional autoencoders Polytopic LPV system Convex polytope 65F45 68T07 Model order reduction Clustering Linear parameter-varying (LPV) systems
ISSN:	0021-9991
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