Hierarchical Bayesian constitutive model selection for high-strain-rate soft material characterization

2025/11/20 by Víctor Sánchez, Sawyer Remillard, Sanchez, Victor +13
Engineering · Materials Science · #Computational Physics (physics.comp-ph) #Elasticity and Material Modeling #FOS: Physical sciences #Fluid Dynamics (physics.flu-dyn) #Soft Robotics and Applications #Ultrasound and Cavitation Phenomena

paper · doi:10.48550/arxiv.2511.16794

openalex publication_date 2025/11/20 · openalex created_date 2025/11/25 · openalex updated_date 2026/07/28

Abstract

(MAP) material parameter values. Synthetic tests recover the ground-truth models and expected parameters. Using experimental data for gelatin, fibrin, polyacrylamide, and agarose, MAP simulations of credible models reproduce the data. Moreover, a cross-institutional comparison of 10% gelatin indicates consistent constitutive model selection.

Citations

• Accelerating Bayesian Optimal Experimental Design via Local Radial Basis Functions: Application to Soft Material Characterization
• Bayesian optimal design accelerates discovery of material properties from bubble dynamics
• Physics-informed Data-driven Discovery of Constitutive Models with Application to Strain-Rate-sensitive Soft Materials
• Parsimonious inertial cavitation rheometry via bubble collapse time
• SerialTrack: ScalE and Rotation Invariant Augmented Lagrangian Particle Tracking
• Model selection and sensitivity analysis in the biomechanics of soft tissues: a case study on the human knee meniscus
• Identification of the parameters of complex constitutive models: Least squares minimization vs. Bayesian updating
• Bubble Dynamics and Cavitation

Cited by

• Transformer-Based Inverse Microrheology for Experimental Mechanics at Ultra-High Strain Rates

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