Unsupervised Material Fingerprinting: Ultra-fast hyperelastic model discovery from full-field experimental measurements

TL;DR

This paper introduces an unsupervised material fingerprinting method that rapidly discovers hyperelastic material models from full-field experimental data without optimization, validated through experimental results.

Contribution

It presents the first experimental validation of unsupervised material fingerprinting using full-field displacement data for hyperelastic materials.

Findings

Fast model discovery from experimental data

No need for optimization or solving inverse problems

Validated with real experimental measurements

Abstract

Material Fingerprinting is a lookup table-based strategy to discover material models from experimental measurements, which completely avoids the need to solve an optimization problem. In an offline phase, a comprehensive database of simulated material responses, so-called material fingerprints, is generated for a predefined experimental setup. This database can then be used repeatedly in the online phase to discover material models corresponding to experimentally measured observations. To this end, the experimentally measured fingerprint is compared with all fingerprints in the database to identify the closest match. The primary advantage of this strategy is that it does not require solving a continuous optimization problem. This avoids the associated computational costs as well as issues of ill-posedness caused by local minima in non-convex optimization landscapes. Material Fingerprinting has been successfully demonstrated for supervised datasets consisting of stress-strain pairs, as well as for unsupervised datasets involving full-field displacements and net reaction forces. However, to date, there is no experimental validation for the latter approach which is the objective of this work.