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Role of calibration, validation, and relevance in multi-level uncertainty integration

机译：校准，验证和相关性在多级不确定性积分中的作用

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摘要

Calibration of model parameters is an essential step in predicting the response of a complicated system, but the lack of data at the system level makes it impossible to conduct this quantification directly. In such a situation, system model parameters are estimated using tests at lower levels of complexity which share the same model parameters with the system. For such a multi-level problem, this paper proposes a methodology to quantify the uncertainty in the system level prediction by integrating calibration, validation and sensitivity analysis at different levels. The proposed approach considers the validity of the models used for parameter estimation at lower levels, as well as the relevance at the lower level to the prediction at the system level. The model validity is evaluated using a model reliability metric, and models with multivariate output are considered. The relevance is quantified by comparing Sobol indices at the lower level and system level, thus measuring the extent to which a lower level test represents the characteristics of the system so that the calibration results can be reliably used in the system level. Finally the results of calibration, validation and relevance analysis are integrated in a roll-up method to predict the system output. (C) 2015 Elsevier Ltd. All rights reserved.
机译：模型参数的校准是预测复杂系统响应的必不可少的步骤，但是由于缺乏系统级别的数据，因此无法直接进行量化。在这种情况下，使用较低复杂度的测试估计系统模型参数，这些测试与系统共享相同的模型参数。对于这样一个多层次的问题，本文提出了一种通过集成不同层次的校准，验证和灵敏度分析来量化系统层次预测中不确定性的方法。所提出的方法考虑了用于较低级别的参数估计的模型的有效性，以及较低级别与系统级别的预测的相关性。使用模型可靠性度量评估模型有效性，并考虑具有多变量输出的模型。通过在较低级别和系统级别比较Sobol指数来量化相关性，从而测量较低级别的测试代表系统特征的程度，以便可以在系统级别可靠地使用校准结果。最后，将校准，验证和相关性分析的结果集成到汇总方法中，以预测系统输出。 （C）2015 Elsevier Ltd.保留所有权利。

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