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Data-driven elasto-(visco)-plasticity involving hidden state variables?
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DOI：10.1016/j.cma.2022.115394.png)
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Abstract
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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.(c) 2022 Elsevier B.V. All rights reserved.
Keywords:
Data -driven
History -dependent materials
Computational mechanics
Experimental constitutive manifold
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