A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real-time and many-query evaluations of parametric Partial Differential Equations (PDEs). Linear...

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Vydané v:	Journal of computational physics Ročník 501; s. 112762
Hlavní autori:	Pichi, Federico, Moya, Beatriz, Hesthaven, Jan S.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Inc 15.03.2024
Predmet:	Convolutional autoencoder Geometrical parameters Graph neural networks Nonlinear reduced order modeling Parametric PDEs Nonlinear reduced order modeling Parametric PDEs Graph neural networks Geometrical parameters Convolutional autoencoder
ISSN:	0021-9991, 1090-2716
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