Inverse uncertainty quantification of a mechanical model of arterial tissue with surrogate modeling

TL;DR

This paper presents a Bayesian inverse uncertainty quantification approach for calibrating a microscale arterial tissue model, utilizing surrogate modeling to manage high computational costs and improve agreement with experimental data.

Contribution

It introduces a surrogate-based Bayesian calibration method for arterial tissue models, enhancing efficiency and accuracy in simulating mechanical responses.

Findings

Successful calibration of the tissue model to experimental data

Reduction of uncertainty in model parameters

Effective use of Gaussian process surrogate model

Abstract

Disorders of coronary arteries lead to severe health problems such as atherosclerosis, angina, heart attack and even death. Considering the clinical significance of coronary arteries, an efficient computational model is a vital step towards tissue engineering, enhancing the research of coronary diseases and developing medical treatment and interventional tools. In this work, we applied inverse uncertainty quantification to a microscale agent-based arterial tissue model, a component of a multiscale in-stent restenosis model. Inverse uncertainty quantification was performed to calibrate the arterial tissue model to achieve the mechanical response in line with tissue experimental data. Bayesian calibration with bias term correction was applied to reduce the uncertainty of unknown polynomial coefficients of the attractive force function and achieved agreement with the mechanical behaviour of arterial tissue based on the uniaxial strain tests. Due to the high computational costs of the model, a surrogate model based on Gaussian process was developed to ensure the feasibility of the computation.