Data-driven computation for history-dependent materials

Pierre Ladevèze (1) , David Néron (1) , Paul-William Gerbaud (1)

1 LMT - Laboratoire de Mécanique et Technologie

This paper introduces a new vision of data-driven structure computation taking advantage of Material Science, especially for highly nonlinear and time-dependent material behaviours. Technical solutions are also derived, in order to build internal hidden variables defining the so-called “Experimental Constitutive Manifold”.

Mots clés: Data-driven; History-dependent materials; Computational mechanics; Big data; Experimental constitutive manifold; Material mechanics

Domaines: Mécanique des structures [physics.class-ph]

Pierre Ladevèze, David Néron, Paul-William Gerbaud. Data-driven computation for history-dependent materials. Comptes Rendus Mécanique, 2019, 347 (11), pp.831-844. ⟨10.1016/j.crme.2019.11.008⟩. ⟨hal-02417567⟩