Constructing a Simulation Surrogate with Partially Observed Output
Constructing a Simulation Surrogate with Partially Observed Output
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DOI：
10.1080/00401706.2023.2210170
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发表时间：
2023-04
期刊：
影响因子：
2.5
通讯作者：
Moses Y H Chan;M. Plumlee;Stefan M. Wild
中科院分区：
文献类型：
--
作者：
Moses Y H Chan;M. Plumlee;Stefan M. Wild
摘要高斯过程代理是直接使用计算昂贵的仿真模型的一种流行的替代方案。当仿真输出由多个响应组成时，通常采用降维技术来构造这些代理。然而，降维的代理方法一般依赖于完整的输出训练数据。本文提出了一种新的高斯过程代理方法，该方法允许在保持计算效率的同时使用部分观测输出。新方法包括缺失值的补充和用于高斯过程推断的协方差矩阵的调整。生成的代理代表可用的响应，忽略丢失的响应，并提供有意义的不确定性量化。在一个仿真研究和一个经常返回不完全输出的能量密度泛函模型被校准的案例研究中，所提出的方法被证明提供了比备选方法更准确的推断。
Abstract Gaussian process surrogates are a popular alternative to directly using computationally expensive simulation models. When the simulation output consists of many responses, dimension-reduction techniques are often employed to construct these surrogates. However, surrogate methods with dimension reduction generally rely on complete output training data. This article proposes a new Gaussian process surrogate method that permits the use of partially observed output while remaining computationally efficient. The new method involves the imputation of missing values and the adjustment of the covariance matrix used for Gaussian process inference. The resulting surrogate represents the available responses, disregards the missing responses, and provides meaningful uncertainty quantification. The proposed approach is shown to offer sharper inference than alternatives in a simulation study and a case study where an energy density functional model that frequently returns incomplete output is calibrated.