Work

Data-driven material modeling based on the Constitutive Relation Error

Year: 2024

Type: article

Abstract: 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.

Source: Advanced Modeling and Simulation in Engineering Sciences

DOI: 10.1186/s40323-024-00279-x

Authors: Pierre Ladevèze, Ludovic Chamoin

Institutions: Centre National de la Recherche Scientifique, Université Paris-Saclay, CentraleSupélec, Laboratoire de Mécanique Paris-Saclay, Institut Universitaire de France

Language: English

Topic: Elasticity and Material Modeling

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,790

Funders: European Commission, HORIZON EUROPE European Research Council