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

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 computer 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 apply inverse uncertainty quantification to a microscale agent-based arterial tissue model, a component of the 3D multiscale model of in-stent restenosis (ISR3D). IUQ provides calibration of the arterial tissue model to achieve realistic mechanical behaviour in line with the experimental data measured from the tissue's macroscopic behaviour. Bayesian calibration with bias term correction is applied as an IUQ technique to reduce the uncertainty of unknown polynomial coefficients of the attractive force function and achieve agreement with the experimental data based on the uniaxial strain tests of arterial tissue. Due to the high computational costs of the ISR3D model, the Gaussian process (GP) regression surrogate model is introduced to ensure the feasibility of the IUQ computations. The result is an IUQ methodology to calibrate a model with uncertain parameters and a microscale agent-based model of arterial tissue, which produces mechanical behaviour in line with the experimental data