Robust experimental designs for model calibration
Robust experimental designs for model calibration
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DOI：
10.1080/00224065.2021.1930618
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发表时间：
2020-08
影响因子：
2.5
通讯作者：
A. Krishna;V. R. Joseph;Shan Ba;William A. Brenneman;William R. Myers Georgia Institute of Technology;LinkedIn Corporation;ProcterGamble Company
中科院分区：
文献类型：
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作者：
A. Krishna;V. R. Joseph;Shan Ba;William A. Brenneman;William R. Myers Georgia Institute of Technology;LinkedIn Corporation;ProcterGamble Company
计算机模型只有在确定了某些未知的物理常数（称为校准参数）的值之后，才能用于预测输出。未知的标定参数可以通过进行物理实验从真实的数据中估计。本文提出了一种优化设计此类物理实验的方法。使用计算机模型优化设计物理实验的问题类似于找到拟合非线性模型的最优设计的问题。然而，这个问题比现有的非线性优化设计工作更具挑战性，因为模型差异的可能性，也就是说，计算机模型可能不是真正的底层模型的准确表示。因此，我们提出了一个最佳的设计方法，是强大的潜在的模型差异。我们表明，我们的设计是优于常用的物理实验设计，不利用计算机模型中包含的信息和其他非线性优化设计，忽略潜在的模型差异。我们用一个玩具的例子和一个来自工业界的真实的例子来说明我们的方法。
Abstract A computer model can be used for predicting an output only after specifying the values of some unknown physical constants known as calibration parameters. The unknown calibration parameters can be estimated from real data by conducting physical experiments. This paper presents an approach to optimally design such a physical experiment. The problem of optimally designing a physical experiment, using a computer model, is similar to the problem of finding an optimal design for fitting nonlinear models. However, the problem is more challenging than the existing work on nonlinear optimal design because of the possibility of model discrepancy, that is, the computer model may not be an accurate representation of the true underlying model. Therefore, we propose an optimal design approach that is robust to potential model discrepancies. We show that our designs are better than the commonly used physical experimental designs that do not make use of the information contained in the computer model and other nonlinear optimal designs that ignore potential model discrepancies. We illustrate our approach using a toy example and a real example from industry.