The language of hyperelastic materials

Abstract

The automated discovery of constitutive laws forms an emerging research 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 the availability of libraries of material models, which are typically hand-designed by a human expert using 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. Compliance with physics constraints is partly enforced a priori and partly empirically checked a posteriori. 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 avoiding hand-crafted features and human intervention. For the data-driven discovery task, we demonstrate the accuracy, robustness and significant generalizability of the proposed methodology. • We propose the Grammar of Hyperelastic Materials, designed to derive valid material laws. • We use the Grammar for the automatic generation of a library of hyperelastic material laws. • We include the Grammar in a symbolic regression pipeline for data-driven discovery of material laws. • Data-driven discovery is performed for different datasets and noise levels without re-training.

Topics & Concepts