SIAM/ASA Journal on Uncertainty Quantification, Volume 9, Issue 2, Page 333-353, January 2021.
Computer experiments with both quantitative and qualitative inputs are commonly used in science and engineering applications. Constructing desirable emulators for such computer experiments remains a challenging problem. In this article, we propose an easy-to-interpret Gaussian process (EzGP) model for computer experiments to reflect the change of the computer model under the different level combinations of qualitative factors. The proposed modeling strategy, based on an additive Gaussian process, is flexible to address the heterogeneity of computer models involving multiple qualitative factors. We also develop two useful variants of the EzGP model to achieve computational efficiency for data with high dimensionality and large sizes. The merits of these models are illustrated by several numerical examples and a real data application.

中文翻译：

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EzGP：易于解释的具有定量和定性因素的计算机实验高斯过程模型

SIAM / ASA不确定性量化期刊，第9卷，第2期，第333-353页，2021年1月。
具有定量和定性输入的计算机实验通常在科学和工程应用中使用。为此类计算机实验构建理想的仿真器仍然是一个具有挑战性的问题。在本文中，我们提出了一种易于解释的高斯过程（EzGP）模型用于计算机实验，以反映在定性因素的不同层次组合下计算机模型的变化。所提出的建模策略基于加性高斯过程，可以灵活解决涉及多个定性因素的计算机模型的异质性。我们还开发了EzGP模型的两个有用的变体，以实现具有高维和大尺寸数据的计算效率。这些模型的优点通过几个数值示例和实际数据应用得以说明。