Data-driven elasto-(visco)-plasticity involving hidden state variables

The paper deals with a fundamental problem at the core of a data-driven approach for history-dependent materials: how to compute the hidden state variables related to the material memory from experimental data. This problem, already introduced in Ladeveze (2019, 2022), is transformed here to be solv...

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Bibliographic Details
Published in:Computer methods in applied mechanics and engineering 2022-12, Vol.402, p.115394, Article 115394
Main Authors: Gerbaud, Paul-William, Néron, David, Ladevèze, Pierre
Format: Article
Language:English
Subjects:
Computational mechanics
Data-driven
Engineering Sciences
Experimental constitutive manifold
History-dependent materials
Mechanics
Numerical methods
State variable
Structural mechanics
Two dimensional analysis
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Summary:The paper deals with a fundamental problem at the core of a data-driven approach for history-dependent materials: how to compute the hidden state variables related to the material memory from experimental data. This problem, already introduced in Ladeveze (2019, 2022), is transformed here to be solved by classical numerical methods. These additional state variables allow the Experimental Constitutive Manifold, built from experimental data, to be a consistent data-driven material model. The proposed computational method is described and analyzed on 2D problems for which experimental data are simulated using classical elastic-(visco)-plastic models.
ISSN:0045-7825
1879-2138
DOI:10.1016/j.cma.2022.115394