A non-intrusive nonlinear model reduction method for structural dynamical problems based on machine learning

dc.contributor	Cluster of Excellence Simulation Technology, University of Stuttgart - EXC 2075	de
dc.contributor.author	Kneifl, Jonas
dc.contributor.author	Grunert, Dennis
dc.contributor.author	Fehr, Jörg
dc.date.accessioned	2020-12-11T08:27:37Z
dc.date.available	2020-12-11T08:27:37Z
dc.date.issued	2020	de
dc.description.abstract	The paper uses a nonlinear non-intrusive model reduction approach, to derive efficient and accurate surrogate models for structural dynamical problems. Therefore, a combination of proper orthogonal decomposition along with regression algorithms from the field of machine learning is utilized to capture the dynamics in a reduced representation. This allows highly performant approximations of the original system. In this context, we provide a comparison of several regression algorithms based on crash simulations of a structural dynamic frame.	en
dc.identifier.uri	http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-111984	de
dc.identifier.uri	http://elib.uni-stuttgart.de/handle/11682/11198
dc.identifier.uri	http://dx.doi.org/10.18419/opus-11181
dc.language.iso	en	de
dc.rights	info:eu-repo/semantics/openAccess	de
dc.subject.ddc	620	de
dc.title	A non-intrusive nonlinear model reduction method for structural dynamical problems based on machine learning	en
dc.type	preprint	de
ubs.fakultaet	Konstruktions-, Produktions- und Fahrzeugtechnik	de
ubs.institut	Institut für Technische und Numerische Mechanik	de
ubs.publikation.noppn	yes	de
ubs.publikation.seiten	10	de
ubs.publikation.typ	Preprint	de

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