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Efficient two‐scale simulations of microstructured materials using deep material networks

Gajek, Sebastian; Schneider, Matti; Böhlke, Thomas ORCID iD icon


Deep material networks (DMN) are a promising piece of technology for accelerating concurrent multiscale simulations. DMNs are identified by linear elastic pre-computations on representative volume elements, and serve as high-fidelity surrogates for full-field simulations on microstructures with inelastic constituents. The offline training phase is independent of the online evaluation, such that a pre-trained DMN may be applied for varying material behavior of the constituents. In this contribution, we investigate a two-scale component simulation of industrial complexity accelerated by DMNs. To this end, a DMN is solved implicitly at every Gauss point to include the microstructure information into the macro simulation.

Verlagsausgabe §
DOI: 10.5445/IR/1000141794
Veröffentlicht am 11.01.2022
DOI: 10.1002/pamm.202100069
Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Technische Mechanik (ITM)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 12.2021
Sprache Englisch
Identifikator ISSN: 1617-7061, 1617-7061
KITopen-ID: 1000141794
Erschienen in Proceedings in applied mathematics and mechanics
Verlag Wiley-VCH Verlag
Band 21
Heft 1
Seiten Art.-Nr. e202100069
Bemerkung zur Veröffentlichung Special Issue: 92nd Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM)
Vorab online veröffentlicht am 14.12.2021
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