Deep material networks for efficient scale-bridging in thermomechanical simulations of solids

dc.contributor.author	Gajek, Sebastian
dc.date.accessioned	2023-11-16T11:17:59Z
dc.date.available	2023-11-16T11:17:59Z
dc.date.issued	2023
dc.date.submitted	2023-09-04T12:19:03Z
dc.identifier	https://library.oapen.org/handle/20.500.12657/76126
dc.identifier.uri	https://directory.doabooks.org/handle/20.500.12854/121498
dc.description.abstract	We investigate deep material networks (DMN). We lay the mathematical foundation of DMNs and present a novel DMN formulation, which is characterized by a reduced number of degrees of freedom. We present a efficient solution technique for nonlinear DMNs to accelerate complex two-scale simulations with minimal computational effort. A new interpolation technique is presented enabling the consideration of fluctuating microstructure characteristics in macroscopic simulations.
dc.language	English
dc.relation.ispartofseries	Schriftenreihe Kontinuumsmechanik im Maschinenbau
dc.rights	open access
dc.subject.other	deep material networks; data-driven modeling; Two-scale simulations; Deep Material Networks; Datengetriebene Modellierung; Zweiskalensimulationen; micromechanics; Mikromechanik; machine learning; Maschinelles Lernen
dc.title	Deep material networks for efficient scale-bridging in thermomechanical simulations of solids
dc.type	book
oapen.identifier.doi	10.5445/KSP/1000155688
oapen.relation.isPublishedBy	68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2
oapen.pages	326
dc.seriesnumber	26

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