Improving Identifiability in Model Calibration Using Multiple Responses
Improving Identifiability in Model Calibration Using Multiple Responses
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DOI：
10.1115/detc2011-48623
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发表时间：
2012-10
影响因子：
1.2
通讯作者：
Paul D. Arendt;Wei Chen;D. Apley
中科院分区：
文献类型：
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作者：
Paul D. Arendt;Wei Chen;D. Apley
在基于物理的工程建模中，模型不确定性的两个主要来源是参数不确定性和模型差异，这是计算机模型和物理实验之间差异的原因。区分两种不确定性来源的影响可能具有挑战性。对于无法使用单一响应来实现可识别性的情况，我们建议通过使用多个响应来提高可识别性，这些响应共享一组共同的校准参数上的相互依赖性。为此，我们扩展了单响应模块贝叶斯方法计算后验分布的校准参数和差异函数的多个响应。使用一个工程实例，我们证明，包括多个响应可以提高可识别性（后验标准差测量）的范围从最小到实质性的量，这取决于组合的特定响应的特性。
In physics-based engineering modeling, the two primary sources of model uncertainty, which account for the differences between computer models and physical experiments, are parameter uncertainty and model discrepancy. Distinguishing the effects of the two sources of uncertainty can be challenging. For situations in which identifiability cannot be achieved using only a single response, we propose to improve identifiability by using multiple responses that share a mutual dependence on a common set of calibration parameters. To that end, we extend the single response modular Bayesian approach for calculating posterior distributions of the calibration parameters and the discrepancy function to multiple responses. Using an engineering example, we demonstrate that including multiple responses can improve identifiability (as measured by posterior standard deviations) by an amount that ranges from minimal to substantial, depending on the characteristics of the specific responses that are combined.