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.

翻译：冠状动脉疾病会导致严重的健康问题，例如动脉粥样硬化、心绞痛、心脏病发作甚至死亡。鉴于冠状动脉的临床重要性，高效的计算模型是组织工程的关键步骤，有助于推动冠状动脉疾病的研究以及医疗和介入工具的开发。本研究将逆不确定性量化应用于微观尺度基于智能体的动脉组织模型，该模型是多尺度支架内再狭窄模型的一个组成部分。通过逆不确定性量化校准动脉组织模型，使其力学响应与组织实验数据一致。采用带偏差项校正的贝叶斯校准来减少吸引力函数中未知多项式系数的不确定性，并根据单轴应变测试结果实现了与动脉组织力学行为的一致性。由于模型计算成本较高，我们基于高斯过程开发了替代模型以确保计算的可行性。