Material Fingerprinting for rapid discovery of hyperelastic models: First experimental validation

Abstract

Material Fingerprinting is an emerging approach for the rapid discovery of mechanical material models directly from experimental data. By interpreting a material’s response in standardized experiments as its fingerprint, Material Fingerprinting employs pattern recognition to match experimental data against a precomputed database, enabling real-time model discovery. This strategy is both fast and robust, as it avoids solving potentially non-convex optimization problems. Unlike traditional calibration methods, Material Fingerprinting simultaneously selects the most suitable material model and identifies its parameters. Since the fingerprint database is fully controllable, the method guarantees interpretable and physically meaningful models. In previous work, we showed the feasibility of this concept for experiments with homogeneous or heterogeneous deformation fields using synthetically generated data. Here we present the first experimental validation of Material Fingerprinting. We carefully design a fingerprint database for uniaxial tension/compression, equibiaxial tension as well as pure and simple shear experiments. Once computed in an offline phase, this database can be reused for rapid model discovery across diverse experimental datasets. We demonstrate that this single database enables the robust and efficient discovery of hyperelastic strain energy functions to accurately characterize the isotropic mechanical responses of rubber, hydrogel, and brain tissue in less than one second on a standard personal computer. To make this approach openly accessible for rapid material model discovery across laboratories, we release the database and the implementation of Material Fingerprinting as a pip-installable Python package alongside this publication.