Calibrating constitutive models with full‐field data via physics informed neural networks

Hamel, Craig M.; Long, Kevin N.; Kramer, Sharlotte L. B.

doi:10.1111/str.12431

Title: Calibrating constitutive models with full‐field data via physics informed neural networks

Journal Article · 19 November 2022 · Strain

DOI: https://doi.org/10.1111/str.12431 · OSTI ID:1962922

^[1]; Long, Kevin N. ^[1]; Kramer, Sharlotte L. B. ^[2]

Materials and Failure Modeling Department Sandia National Laboratories Albuquerque New Mexico USA
Experimental Solid Mechanics Department Sandia National Laboratories Albuquerque New Mexico USA

Abstract The calibration of solid constitutive models with full‐field experimental data is a long‐standing challenge, especially in materials that undergo large deformations. In this paper, we propose a physics‐informed deep‐learning framework for the discovery of hyperelastic constitutive model parameterizations given full‐field surface displacement data and global force‐displacement data. Contrary to the majority of recent literature in this field, we work with the weak form of the governing equations rather than the strong form to impose physical constraints upon the neural network predictions. The approach presented in this paper is computationally efficient, suitable for irregular geometric domains, and readily ingests displacement data without the need for interpolation onto a computational grid. A selection of canonical hyperelastic material models suitable for different material classes is considered including the Neo–Hookean, Gent, and Blatz–Ko constitutive models as exemplars for general non‐linear elastic behaviour, elastomer behaviour with finite strain lock‐up, and compressible foam behaviour, respectively. We demonstrate that physics informed machine learning is an enabling technology and may shift the paradigm of how full‐field experimental data are utilized to calibrate constitutive models under finite deformations.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: NA0003525

OSTI ID:: 1962922

Journal Information:: Strain, Journal Name: Strain Journal Issue: 2 Vol. 59; ISSN 0039-2103

Publisher:: Wiley-BlackwellCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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