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dc.contributor.authorGajek, Sebastian
dc.date.accessioned2023-09-04T12:19:03Z
dc.date.available2023-09-04T12:19:03Z
dc.date.issued2023
dc.identifierOCN: 1402511918
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/76126
dc.description.abstractWe 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.en_US
dc.languageEnglishen_US
dc.relation.ispartofseriesSchriftenreihe Kontinuumsmechanik im Maschinenbauen_US
dc.subject.otherdeep material networks; data-driven modeling; Two-scale simulations; Deep Material Networks; Datengetriebene Modellierung; Zweiskalensimulationen; micromechanics; Mikromechanik; machine learning; Maschinelles Lernenen_US
dc.titleDeep material networks for efficient scale-bridging in thermomechanical simulations of solidsen_US
dc.typebook
oapen.identifier.doi10.5445/KSP/1000155688en_US
oapen.relation.isPublishedBy44e29711-8d53-496b-85cc-3d10c9469be9en_US
oapen.series.number26en_US
oapen.pages326en_US
peerreview.anonymityAll identities known
peerreview.id51a542ec-eaeb-47c2-861d-6022e981a97a
peerreview.open.reviewNo
peerreview.publish.responsibilityBooks or series editor
peerreview.review.stagePre-publication
peerreview.review.typeFull text
peerreview.reviewer.typeEditorial board member
peerreview.reviewer.typeExternal peer reviewer
peerreview.titleDissertations in Series (Dissertationen in Schriftenreihe)


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