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The rapid and accurate characterization of soft, viscoelastic materials at high strain rates is of interest in biological and engineering applications such as assessing the extent of non-invasive tissue surgery completion and developing injury criteria for the mitigation of blast injuries. The inertial microcavitation rheometry technique (IMR, Estrada et al. 2018) allows for the minimally invasive characterization of local viscoelastic properties at strain rates up to 1E8 per second. However, IMR relies on bright-field videography of a sufficiently translucent sample at approximately 1 million frames per second and a simulation-dependent fit optimization process that can require hours of post-processing. We present an IMR-style technique that parsimoniously characterizes viscoelastic models. The approach uses experimental advancements to accurately estimate the time to first collapse of the laser-induced cavity. A theoretical energy balance analysis yields an approximate collapse time based on the material viscoelasticity parameters. The method closely matches the accuracy of the original IMR procedure while decreasing the computational cost from hours to seconds for the Kelvin--Voigt model and potentially reducing dependence on high-speed videography. This technique can enable nearly real-time characterization of a broader range of soft, viscoelastic hydrogels and biological materials.

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., Comment: 19 pages, 8 figures