Data Driven Constitutive Model Discovery from In Silico Myocardial Biaxial Stretch Tests

Abstract

This thesis investigates the ability of Bayesian EUCLID to retrieve a predictive approximate material model for the myocardium in the presence of heterogeneous deformation fields due to simulated biaxial stretch tests. The Holzapfel-Ogden material model is used as the ground-truth material model of the simulation, since it is capable of describing the orthotropic and hyperelastic behavior of the myocardium. In total, four material models were retrieved from Bayesian-EUCLID by considering two fiber orientations within the sample and two displacement measurement techniques. The two fiber directions included one in which the sheet-like structure of the sample lies within the test plane and one in which it lies perpendicular to the test plane. The displacement field measurements considered were a full displacement field measurement and an approximation thereof based on a stereo digital image correlation method. The results show that when the sheet-like structure of the myocardium lies within the test plane of the biaxial stretch test and the full displacement field is available, Bayesian EUCLID is capable of finding orthotropic and hyperelastic material models that correspond well to the ground truth. This shows that a biaxial stretch test can adequately characterize the material behavior of the myocardium.