Parameter Screening in Statistical Dynamic Computer Model Calibration Using Global Sensitivities
Parameter Screening in Statistical Dynamic Computer Model Calibration Using Global Sensitivities
复制标题
使用全局灵敏度的统计动态计算机模型校准中的参数筛选
DOI：
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发表时间：
2012
期刊：
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通讯作者：
Z. Mourelatos
中科院分区：
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作者：
Dorin Drignei;Z. Mourelatos
计算机或模拟模型在科学和工程中无处不在。灵敏度分析和计算机模型校准是建立计算机模型的两个研究课题，通常是分开处理的。在灵敏度分析中，要量化每个输入因素对输出的影响，而在校准中，要找到与一组测试数据最匹配的输入因素的值。在本文中，我们将展示这两个看似独立的概念之间的联系，以解决瞬态信号问题。我们对具有瞬态信号的计算机模型使用全局灵敏度分析来筛选非活动输入因素，从而使校准算法在数值上更加稳定。我们表明，计算机模型不随总灵敏度指数为零的参数而变化，这表明这些参数是不可能校准的，必须筛选出来。由于计算机模型计算量大，我们构建了一个快速的计算机模型统计代理，用于灵敏度分析和计算机模型校准。我们用一个简单的例子和一个涉及道路载荷数据采集（RLDA）计算机模型的汽车应用程序来说明我们的方法。
Computer, or simulation, models are ubiquitous in science and engineering. Two research topics in building computer models, generally treated separately, are sensitivity analysis and computer model calibration. In sensitivity analysis, one quantifies the effect of each input factor on outputs, whereas in calibration, one finds the values of input factors that provide the best match to a set of test data. In this article, we show a connection between these two seemingly separate concepts for problems with transient signals. We use global sensitivity analysis for computer models with transient signals to screen out inactive input factors, thus making the calibration algorithm numerically more stable. We show that the computer model does not vary with respect to parameters having zero total sensitivity indices, indicating that such parameters are impossible to calibrate and must be screened out. Because the computer model can be computationally intensive, we construct a fast statistical surrogate of the computer model which is used for both sensitivity analysis and computer model calibration. We illustrate our approach with both a simple example and an automotive application involving a road load data acquisition (RLDA) computer model.