In order to accurately describe the mechanics of red blood cells (RBCs) and resulting fluid dynamics, a cell-resolved blood flow fluid solver is required. The parameters of the material model for the RBC membranes are carefully tuned to reproduce the behavior of real cells under various experimental conditions. In this work, uncertainty in the parameters of the material model for RBCs used in a model for RBC suspensions was estimated with Inverse Uncertainty Quantification (IUQ) using Bayesian Annealed Sequential Importance Sampling (BASIS). Due to the relatively high computational cost of the model, a Gaussian Process regression metamodel was trained in order to feasibly draw the large number of samples required to obtain an accurate posterior distribution estimate. Additionally, the identifiability of the model parameters was estimated using Sobol sensitivity indices. The elongation index of simulated RBCs in a perfect sheared environment was the model prediction used to calibrate model parameters. The results show good identifiability of the parameter defining the tensile properties of the cell membrane and viscosity ratio, and poor identifiability of the parameter defining the response of the cell surface while undergoing bending. This suggests that the latter should be identified using a different quantity of interest. Overall, the model outputs with the optimal values of the parameters obtained using the Gaussian Process metamodel match better or close to the measurements than the results with the parameters' values obtained with the original model. Therefore, we can conclude that it is a valid method to decrease the computational cost of IUQ of the model.

中文翻译：

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使用高斯过程元模型对细胞模型进行逆不确定性量化

为了准确描述红细胞（RBC）的力学及其产生的流体动力学，需要一种细胞分解的血流流体求解器。仔细调整了RBC膜的材料模型参数，以重现真实细胞在各种实验条件下的行为。在这项工作中，使用贝叶斯退火顺序重要性抽样（BASIS），通过逆不确定性量化（IUQ）估计了RBC悬架模型中所用RBC的材料模型参数的不确定性。由于该模型的计算成本较高，因此对高斯过程回归元模型进行了训练，以切实可行地绘制获得准确的后验分布估计所需的大量样本。另外，使用Sobol灵敏度指标估算模型参数的可识别性。在理想剪切环境下，模拟红细胞的伸长指数是用于校准模型参数的模型预测。结果表明，定义细胞膜的拉伸性能和粘度比的参数的可识别性好，而定义细胞表面在弯曲时的响应的参数的可识别性差。这表明应使用不同数量的利息来识别后者。总体而言，与使用原始模型获得的参数值的结果相比，使用高斯过程元模型获得的参数的最优值的模型输出与测量结果的匹配更好或更接近。因此，