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The Language of Hyperelastic Materials

by G. Kissas and S. Mishra and E. Chatzi and L. De Lorenzis

(Report number 2024-07)

Abstract
The automated discovery of constitutive laws forms an emerging area that focuses on automatically obtaining symbolic expressions describing the constitutive behavior of solid materials from experimental data. Existing symbolic/sparse regression methods rely on availability of libraries of material models, which are typically hand-designed by a human expert relying on known models as reference, or deploy generative algorithms with exponential complexity which are only practicable for very simple expressions. In this paper, we propose a novel approach to constitutive law discovery relying on formal grammars as an automated and systematic tool to generate constitutive law expressions complying with physics constraints. We deploy the approach for two tasks: i) Automatically generating a library of valid constitutive laws for hyperelastic isotropic materials; ii) Performing data-driven discovery of hyperelastic material models from displacement data affected by different noise levels. For the task of automatic library generation, we demonstrate the flexibility and efficiency of the proposed methodology in alleviating hand-crafted features and human intervention. For the data-driven discovery task, we demonstrate the accuracy, robustness and significant generalizability of the proposed methodology.

Keywords: Automated Model Discovery, Data-Driven Constitutive Models, Formal Grammars , Symbolic Regression, Generative AI

@Techreport{KMCD24_1089,
  author = {G. Kissas and S. Mishra and E. Chatzi and L. De Lorenzis},
  title = {The Language of Hyperelastic Materials},
  institution = {Seminar for Applied Mathematics, ETH Z{\"u}rich},
  number = {2024-07},
  address = {Switzerland},
  url = {https://www.sam.math.ethz.ch/sam_reports/reports_final/reports2024/2024-07.pdf },
  year = {2024}
}

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