descriptionPublicationkeyboard_double_arrow_right Article , Conference object 01 Jan 2022Embargo end date: 12 Sep 2022 English Publisher:Elsevier BVJournal:IFAC-PapersOnLine, volume 55, pages 430-435 (issn: 2405-8963,

Authors: Heiland, Jan; Kim, Yongho;

doi: 10.1016/j.ifacol.2022.11.091 , 10.5281/zenodo.6817441 , 10.5281/zenodo.6817442

handle: 21.11116/0000-000B-9F9A-5 , 21.11116/0000-000B-9FAB-2

Convolutional Autoencoders and Clustering for Low-dimensional Parametrization of Incompressible Flows

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Abstract

We provide the complete code base of the presented approaches in the paper. Jan Heiland and Yongho Kim (2022), Convolutional Auto Encoders and Clustering for Low-dimensional Parametrization of Incompressible Flows, 25th International Symposium on Mathematical Theory of Networks and Systems (MTNS)

Related Organizations

Max Planck Institute for Dynamics of Complex Technical Systems
Germany

Keywords

convolutional autoencoders, model reduction, incompressible flows, linear parameter-varying systems, clustering

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