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Learning data-driven reduced elastic and inelastic models of spot-welded patches

Abstract : 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.
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Contributor : Edp Sciences <>
Submitted on : Friday, April 30, 2021 - 10:08:31 PM
Last modification on : Thursday, May 6, 2021 - 9:01:41 AM


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Agathe Reille, Victor Champaney, Fatima Daim, Yves Tourbier, Nicolas Hascoet, et al.. Learning data-driven reduced elastic and inelastic models of spot-welded patches. Mechanics & Industry, EDP Sciences, 2021, 22, pp.32. ⟨10.1051/meca/2021031⟩. ⟨hal-03214183⟩



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