利用 Dempster-Shafer 理论对蠕动心脏泵送进行混合不确定性分析。

IF 2.2 4区 数学 Q2 BIOLOGY Journal of Mathematical Biology Pub Date : 2024-06-16 DOI:10.1007/s00285-024-02116-6
Yanyan He, Nicholas A Battista, Lindsay D Waldrop
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引用次数: 0

摘要

本文介绍了基于概率论和 Dempster-Shafer 理论的混合不确定性传播数值策略,并将其应用于心脏泵送系统蠕动的计算模型。具体来说,系统中的随机不确定性用随机变量表示,而认识上的不确定性则用带有信念函数的非概率不确定变量表示。混合不确定性通过系统传播,导致所选相关量(QoI,如流量、运输成本和功耗)的不确定性。通过引入的数值方法,QoIs 统计中的不确定性将使用信念函数来表示。利用与信念结构一致的三个代表性概率分布,还实施了全局敏感性分析,以确定重要的不确定因素,并对不同蠕动模型的结果进行了比较。为了降低计算成本,采用了物理约束广义多项式混沌法来构建更便宜的代用模型,作为完整模拟的近似值。
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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Mixed uncertainty analysis on pumping by peristaltic hearts using Dempster-Shafer theory.

In this paper, we introduce the numerical strategy for mixed uncertainty propagation based on probability and Dempster-Shafer theories, and apply it to the computational model of peristalsis in a heart-pumping system. Specifically, the stochastic uncertainty in the system is represented with random variables while epistemic uncertainty is represented using non-probabilistic uncertain variables with belief functions. The mixed uncertainty is propagated through the system, resulting in the uncertainty in the chosen quantities of interest (QoI, such as flow volume, cost of transport and work). With the introduced numerical method, the uncertainty in the statistics of QoIs will be represented using belief functions. With three representative probability distributions consistent with the belief structure, global sensitivity analysis has also been implemented to identify important uncertain factors and the results have been compared between different peristalsis models. To reduce the computational cost, physics constrained generalized polynomial chaos method is adopted to construct cheaper surrogates as approximations for the full simulation.

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来源期刊
CiteScore
3.30
自引率
5.30%
发文量
120
审稿时长
6 months
期刊介绍: The Journal of Mathematical Biology focuses on mathematical biology - work that uses mathematical approaches to gain biological understanding or explain biological phenomena. Areas of biology covered include, but are not restricted to, cell biology, physiology, development, neurobiology, genetics and population genetics, population biology, ecology, behavioural biology, evolution, epidemiology, immunology, molecular biology, biofluids, DNA and protein structure and function. All mathematical approaches including computational and visualization approaches are appropriate.
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