Please use this identifier to cite or link to this item: http://dx.doi.org/10.18419/opus-11181
Authors: Kneifl, Jonas
Grunert, Dennis
Fehr, Jörg
Title: A non-intrusive nonlinear model reduction method for structural dynamical problems based on machine learning
Issue Date: 2020
metadata.ubs.publikation.typ: Preprint
metadata.ubs.publikation.seiten: 10
URI: http://elib.uni-stuttgart.de/handle/11682/11198
http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-111984
http://dx.doi.org/10.18419/opus-11181
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.
Appears in Collections:07 Fakultät Konstruktions-, Produktions- und Fahrzeugtechnik

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