Ogden Material Calibration via Magnetic Resonance Cartography, Parameter Sensitivity, and Variational System Identification

TL;DR

This paper introduces a novel approach combining magnetic resonance cartography, PDE-constrained optimization, and statistical testing for robust, interpretable, and parsimonious parameter identification in Ogden material models using 3D displacement data.

Contribution

It develops a comprehensive system identification framework for Ogden materials that integrates magnetic resonance data, local deformation analysis, and statistical criteria for model simplicity.

Findings

Successful application of magnetic resonance cartography to acquire detailed 3D displacement fields.

Effective variational system identification of Ogden model parameters using PDE-constrained optimization.

Insights into experimental design through sensitivity analysis and local deformation decomposition.

Abstract

Contemporary material characterisation techniques that leverage deformation fields and the weak form of the equilibrium equations face challenges in the numerical solution procedure of the inverse characterisation problem. As material models and descriptions differ, so too must the approaches for identifying parameters and their corresponding mechanisms. The widely-used Ogden material model can be comprised of a chosen number of terms of the same mathematical form, which presents challenges of parsimonious representation, interpretability, and stability. Robust techniques for system identification of any material model are important to assess and improve experimental design, in addition to their centrality to forward computations. Using fully 3D displacement fields acquired in silicone elastomers with our recently-developed magnetic resonance cartography (MR-u) technique on the order of $>20,000$ points per sample, we leverage PDE-constrained optimisation as the basis of variational system identification of our material parameters. We incorporate the statistical F-test to maintain parsimony of representation. Using a new, local deformation decomposition locally into mixtures of biaxial and uniaxial tensile states, we evaluate experiments based on an analytical sensitivity metric, and discuss the implications for experimental design.