1 research outputs found
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