Model verification, updating, and selection from the constitutive relation error concept

Chamoin, Ludovic; Ladevèze, Pierre

doi:10.1016/bs.aams.2024.08.005

Advances in Applied Mechanics

2024

DOI: 10.1016/bs.aams.2024.08.005

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Model verification, updating, and selection from the constitutive relation error concept

Ludovic Chamoin,

Pierre Ladevèze

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2024

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Data-driven material modeling based on the Constitutive Relation Error

Ladevèze,

Chamoin

2024

Adv. Model. and Simul. in Eng. Sci.

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Prior to any numerical development, the paper objective is to answer first to a fundamental question: what is the mathematical form of the most general data-driven constitutive model for stable materials, taking maximum account of knowledge from physics and materials science? Here we restrict ourselves to elasto-(visco-)plastic materials under the small displacement assumption. The experimental data consists of full-field measurements from a family of tested mechanical structures. In this framework, a general data-driven approach is proposed to learn the constitutive model (in terms of thermodynamic potentials) from data. A key element that defines the proposed data-driven approach is a tool: the Constitutive Relation Error (CRE); the data-driven model is then the minimizer of the CRE. A notable aspect of this procedure is that it leads to quasi-explicit formulations of the optimal constitutive model. Eventually, a modified Constitutive Relation Error is introduced to take measurement noise into account.

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Data-driven material modeling based on the Constitutive Relation Error

Ladevèze,

Chamoin

2024

Adv. Model. and Simul. in Eng. Sci.

View full text Add to dashboard Cite

show abstract

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Model verification, updating, and selection from the constitutive relation error concept

Cited by 1 publication

References 135 publications

Data-driven material modeling based on the Constitutive Relation Error

Data-driven material modeling based on the Constitutive Relation Error

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