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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Bibliographic Details

Published in:	Computers & mathematics with applications (1987) Vol. 175; pp. 49 - 61
Main Authors:	Heiland, Jan, Kim, Yongho
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.12.2024
Subjects:	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 Access:	Get full text
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