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

• Main Authors: Reille Agathe, Champaney Victor, Daim Fatima, Tourbier Yves, Hascoet Nicolas, Gonzalez David, Cueto Elias, Duval Jean Louis, Chinesta Francisco
• Format: Article
• Language: English
• Published: EDP Sciences 2021-01-01
• Series: Mechanics & Industry
• Subjects: model order reduction; spot-welds; machine learning; artificial intelligence; data-driven mechanics
• Online Access: https://www.mechanics-industry.org/articles/meca/full_html/2021/01/mi210009/mi210009.html

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.