Bayesian Calibration of Nonlinear Cardiovascular Models for the Predictive Simulation of Arterial Growth

The work presented in this thesis deals with the development of a Bayesian calibration framework for parameters of nonlinear computational models of arterial growth. By the incorporation of measurements from longitudinal image data in combination with a novel dimensionality reduction approach, this work demonstrates the feasibility of the predictive use of large-scale, patient-specific computational models for arterial growth. The probabilistic formulation provides a basis for the prospective application of such models in the clinical management routine.