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

Simulations of large-scale dynamical systems require expensive computations and large amounts of storage. Low-dimensional representations of high-dimensional states such as in reduced order models deriving from, say, Proper Orthogonal Decomposition (POD) trade in a reduced model complexity against a...

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Veröffentlicht in:	Computers & mathematics with applications (1987) Jg. 175; S. 49 - 61
Hauptverfasser:	Heiland, Jan, Kim, Yongho
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.12.2024
Schlagworte:	Clustering Convolutional autoencoders Linear parameter varying (LPV) systems Model order reduction Linear parameter varying (LPV) systems Model order reduction Convolutional autoencoders Clustering
ISSN:	0898-1221
Online-Zugang:	Volltext
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