使用高斯过程元模型的细胞模型的逆不确定性量化

IF 1.5 4区工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY

International Journal for Uncertainty Quantification Pub Date : 2020-01-01 DOI:10.1615/int.j.uncertaintyquantification.2020033186

K. D. Vries, A. Nikishova, B. Czaja, Gábor Závodszky, A. Hoekstra

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引用次数: 3

摘要

为了准确地描述红细胞(rbc)的力学和由此产生的流体动力学，需要一个细胞分辨率的血流流体求解器。红细胞膜材料模型的参数经过仔细调整，以再现真实细胞在各种实验条件下的行为。在这项工作中，红细胞悬浮液模型中使用的红细胞材料模型参数的不确定性通过使用贝叶斯退火顺序重要性抽样(BASIS)的逆不确定性量化(IUQ)进行估计。由于模型的计算成本相对较高，为了能够可行地抽取大量样本以获得准确的后验分布估计，我们训练了高斯过程回归元模型。此外，利用Sobol敏感性指数估计模型参数的可辨识性。模拟红细胞在完美剪切环境下的伸长率指数作为模型预测值，用于标定模型参数。结果表明，定义细胞膜拉伸性能和黏度比的参数可识别性较好，而定义细胞表面弯曲响应的参数可识别性较差。这表明，后者应该使用不同的兴趣量来识别。总体而言，使用高斯过程元模型获得的参数最优值的模型输出比使用原始模型获得的参数值的结果更好或更接近测量值。因此，我们可以得出结论，这是降低模型IUQ计算成本的有效方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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INVERSE UNCERTAINTY QUANTIFICATION OF A CELL MODEL USING A GAUSSIAN PROCESS METAMODEL

Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.

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来源期刊

International Journal for Uncertainty Quantification ENGINEERING, MULTIDISCIPLINARY-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

CiteScore

3.60

自引率

5.90%

发文量

期刊介绍： The International Journal for Uncertainty Quantification disseminates information of permanent interest in the areas of analysis, modeling, design and control of complex systems in the presence of uncertainty. The journal seeks to emphasize methods that cross stochastic analysis, statistical modeling and scientific computing. Systems of interest are governed by differential equations possibly with multiscale features. Topics of particular interest include representation of uncertainty, propagation of uncertainty across scales, resolving the curse of dimensionality, long-time integration for stochastic PDEs, data-driven approaches for constructing stochastic models, validation, verification and uncertainty quantification for predictive computational science, and visualization of uncertainty in high-dimensional spaces. Bayesian computation and machine learning techniques are also of interest for example in the context of stochastic multiscale systems, for model selection/classification, and decision making. Reports addressing the dynamic coupling of modern experiments and modeling approaches towards predictive science are particularly encouraged. Applications of uncertainty quantification in all areas of physical and biological sciences are appropriate.