Motivated by a computer model calibration problem from the oil and gas industry, involving the design of a honeycomb seal, we develop a new Bayesian methodology to cope with limitations in the canonical apparatus stemming from several factors. We propose a new strategy of on-site design and surrogate modeling for a computer simulator acting on a high-dimensional input space that, although relatively speedy, is prone to numerical instabilities, missing data, and nonstationary dynamics. Our aim is to strike a balance between data-faithful modeling and computational tractability in a calibration framework--tailoring the computer model to a limited field experiment. Situating our on-site surrogates within the canonical calibration apparatus requires updates to that framework. We describe a novel yet intuitive Bayesian setup that carefully decomposes otherwise prohibitively large matrices by exploiting the sparse blockwise structure. Empirical illustrations demonstrate that this approach performs well on toy data and our motivating honeycomb example.

中文翻译：

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用于大规模校准的现场替代品

受石油和天然气行业的计算机模型校准问题（涉及蜂窝密封设计）的启发，我们开发了一种新的贝叶斯方法来应对由多种因素引起的规范装置的局限性。我们为计算机模拟器提出了一种新的现场设计和代理建模策略，该策略作用于高维输入空间，虽然速度相对较快，但容易出现数值不稳定、数据丢失和非平稳动态。我们的目标是在校准框架中在数据忠实建模和计算易处理性之间取得平衡——根据有限的现场实验定制计算机模型。将我们的现场代理置于规范校准装置中需要对该框架进行更新。我们描述了一种新颖而直观的贝叶斯设置，该设置通过利用稀疏的块状结构仔细分解否则大得令人望而却步的矩阵。实证插图表明，这种方法在玩具数据和我们的激励蜂窝示例上表现良好。