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Disorders of coronary arteries lead to severe health problems such asatherosclerosis, angina, heart attack and even death. Considering the clinicalsignificance of coronary arteries, an efficient computational model is a vitalstep towards tissue engineering, enhancing the research of coronary diseasesand developing medical treatment and interventional tools. In this work, weapplied inverse uncertainty quantification to a microscale agent-based arterialtissue model, a component of a multiscale in-stent restenosis model. Inverseuncertainty quantification was performed to calibrate the arterial tissue modelto achieve the mechanical response in line with tissue experimental data.Bayesian calibration with bias term correction was applied to reduce theuncertainty of unknown polynomial coefficients of the attractive force functionand achieved agreement with the mechanical behaviour of arterial tissue basedon the uniaxial strain tests. Due to the high computational costs of the model,a surrogate model based on Gaussian process was developed to ensure thefeasibility of the computation.

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