Mechanics & Industry
Volume 22, 2021
|Number of page(s)||17|
|Published online||30 April 2021|
Learning data-driven reduced elastic and inelastic models of spot-welded patches
ESI Group, Batiment Seville,
3 bis Saarinen,
2 ESI Group Chair & PIMM Laboratory, Arts et Métiers Institute of Technology, 151 Boulevard de l’Hôpital, 75013 Paris, France
3 Renault, 1 Avenue du Golf, 78084 Guyancourt, France
4 Aragon Institute of Engineering Research, Universidad de Zaragoza, Maria de Luna s/n, 50018 Zaragoza, Spain
* e-mail: firstname.lastname@example.org
Accepted: 8 April 2021
Solving mechanical problems in large structures with rich localized behaviors remains a challenging issue despite the enormous advances in numerical procedures and computational performance. In particular, these localized behaviors need for extremely fine descriptions, and this has an associated impact in the number of degrees of freedom from one side, and the decrease of the time step employed in usual explicit time integrations, whose stability scales with the size of the smallest element involved in the mesh. In the present work we propose a data-driven technique for learning the rich behavior of a local patch and integrate it into a standard coarser description at the structure level. Thus, localized behaviors impact the global structural response without needing an explicit description of that fine scale behaviors.
Key words: Model Order Reduction / Spot-Welds / Machine Learning / Artificial Intelligence / Data-Driven Mechanics
© A. Reille et al., Published by EDP Sciences 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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