A Bayesian approach to selecting hyperelastic constitutive models of soft tissue

DOI10.1016/j.cma.2015.03.012zbMath1423.74624OpenAlexW2059116590MaRDI QIDQ1734491

Bhargava Sista, Sandeep Madireddy, Kumar S. Vemaganti

Publication date: 27 March 2019

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cma.2015.03.012

zbMATH Keywords

model selectionBayesianuncertainty quantificationnested samplinghyperelasticsoft tissue constitutive model

Mathematics Subject Classification ID

Bayesian inference (62F15) Physiology (general) (92C30) Biomechanical solid mechanics (74L15)

Related Items (10)

Stochastic isotropic hyperelastic materials: constitutive calibration and model selection ⋮ Propagation of uncertainty in the mechanical and biological response of growing tissues using multi-fidelity Gaussian process regression ⋮ Bayesian inference of non-linear multiscale model parameters accelerated by a deep neural network ⋮ A Bayesian model calibration framework to evaluate brain tissue characterization experiments ⋮ Bayesian identification and model comparison for random property fields derived from material microstructure ⋮ Hierarchically goal-oriented prediction of skeletal muscle tissue constitutive behavior considering histological characteristics ⋮ Closed‐form stress solutions for incompressible visco‐hyperelastic solids in uniaxial extension ⋮ Auxetic orthotropic materials: numerical determination of a phenomenological spline-based stored density energy and its implementation for finite element analysis ⋮ Anisotropic hyperelastic constitutive models for finite deformations combining material theory and data-driven approaches with application to cubic lattice metamaterials ⋮ A computational framework for Bayesian inference in plasticity models characterisation

Uses Software

• MPI
• MultiNest
• DDDAS

Cites Work

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