Assessment and design of an engineering structure with polymorphic uncertainty quantification
Assessment and design of an engineering structure with polymorphic uncertainty quantification
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DOI：
10.1002/gamm.201900009
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发表时间：
2019-04
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通讯作者：
I. Papaioannou;M. Daub;Martin Drieschner;F. Duddeck;Max Ehre;L. Eichner;M. Eigel;Marco Götz;W. Graf;L. Grasedyck;Robert Gruhlke;D. Hömberg;M. Kaliske;Dieter Moser;Y. Petryna;D. Štraub
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作者：
I. Papaioannou;M. Daub;Martin Drieschner;F. Duddeck;Max Ehre;L. Eichner;M. Eigel;Marco Götz;W. Graf;L. Grasedyck;Robert Gruhlke;D. Hömberg;M. Kaliske;Dieter Moser;Y. Petryna;D. Štraub
工程师面临着在不确定性下支持决策的挑战。工程决策通常取决于对感兴趣的工程系统性能的基于模型的预测。模型的输入不确定性可以分为两种不同的类型：偶然（随机/不可约）或认知（可约）。多态不确定性量化（UQ）可以在一个统一的框架内处理偶然和认知的不确定性。多态UQ框架采用概率理论对偶然变量进行建模，并采用替代方法（区间、模糊、贝叶斯概率及其组合）对认知变量进行建模。本文比较了不同的多态UQ方法，他们的能力，以支持一个简单的工程决策。比较是基于一个试验台的例子，其中偶然变量定义的概率分布和认知变量描述的基础上有限的信息（稀疏数据或区间）。与常见工程决策相关的两个挑战（安全评估和基于可靠性的设计）可作为比较的基础。五个独立的研究小组采用不同的模型来描述的认知参数的基础上的主观解释的给定信息。结果的比较揭示了一个强大的影响的主观选择的认知变量的模型和所选择的基础上获得的决策结果的结构的性能进行评估。
Engineers are faced with the challenge of supporting decision making under uncertainty. Engineering decisions often depend on model‐based predictions of the performance of the engineering system of interest. Input uncertainties of models can be categorized into two distinct types: aleatory (random/irreducible) or epistemic (reducible). Polymorphic uncertainty quantification (UQ) can be used to treat aleatory and epistemic uncertainties in a unified framework. The polymorphic UQ framework employs probability theory to model aleatory variables and alternative approaches (interval, fuzzy, Bayesian probabilistic, and combinations thereof) to model epistemic variables. This paper compares different polymorphic UQ approaches with respect to their ability to support a simple engineering decision. The comparison is based on a test‐bed example, whereby aleatory variables are defined in terms of probability distributions and epistemic variables are described based on limited information (sparse data or intervals). Two challenges related to common engineering decisions (safety assessment and reliability‐based design) serve as a basis for the comparison. Five independent research groups applied different models to describe the epistemic parameters based on a subjective interpretation of the given information. The comparison of the results reveals a strong influence of both the subjective choices on the models of the epistemic variables and the chosen basis for assessing the performance of the structure on the obtained decision outcomes.