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Integration of model verification, validation, and calibration for uncertainty quantification in engineering systems

机译：模型验证，验证和校准的集成，可用于工程系统中的不确定性量化

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This paper proposes a Bayesian methodology to integrate model verification, validation, and calibration activities for the purpose of overall uncertainty quantification in different types of engineering systems. The methodology is first developed for single-level models, and then extended to systems that are studied using multi-level models that interact with each other. Two types of interactions amongst multilevel models are considered: (1) Type-I, where the output of a lower-level model (component and/or subsystem) becomes an input to a higher level system model, and (2) Type-II, where parameters of the system model are inferred using lower-level models and tests (that describe simplified components and/ or isolated physics). The various models, their inputs, parameters, and outputs, experimental data, and various sources of model error are connected through a Bayesian network. The results of calibration, verification, and validation with respect to each individual model are integrated using the principles of conditional probability and total probability, and propagated through the Bayesian network in order to quantify the overall system-level prediction uncertainty. The proposed methodology is illustrated with numerical examples that deal with heat conduction and structural dynamics. (C) 2015 Elsevier Ltd. All rights reserved.
机译：本文提出了一种贝叶斯方法，以集成模型验证，验证和校准活动，以实现不同类型工程系统中总体不确定性量化的目的。该方法首先为单级模型开发，然后扩展到使用相互交互的多级模型进行研究的系统。在多级模型之间考虑了两种类型的交互：（1）Type-I，其中较低级别的模型（组件和/或子系统）的输出成为较高级别的系统模型的输入，以及（2）Type-II ，其中使用较低级别的模型和测试（描述简化的组件和/或孤立的物理原理）来推断系统模型的参数。通过贝叶斯网络连接各种模型，它们的输入，参数和输出，实验数据以及各种模型误差源。使用条件概率和总概率的原理，对每个模型的校准，验证和确认的结果进行整合，并通过贝叶斯网络传播，以量化整个系统级预测的不确定性。通过数值示例说明了所提出的方法，这些示例涉及导热和结构动力学。 （C）2015 Elsevier Ltd.保留所有权利。

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