Mixed-input Gaussian process emulators for computer experiments with a large number of categorical levels. Issue 4 (8th August 2021)

• Record Type: Journal Article
• Authors: Zhang, Qiong; Chien, Peter; Liu, Qing; Xu, Li; Hong, Yili
• Is Part Of: Journal of quality technology. Volume 53:Issue 4(2021)
• Page Start: 410
• Page End: 420
• Publication Date: 2021-08-08
• Subjects: computer experiments -- qualitative and quantitative inputs -- uncertainty quantification; Quality control -- Periodicals; Qualité -- Contrôle -- Périodiques; Quality control; Quality control; Periodicals; 620.0045
• DOI: 10.1080/00224065.2020.1778431
• Languages: English
• ISSNs: 0022-4065

Abstract

Abstract: Computer models with both quantitative and qualitative inputs frequently arise in science, engineering and business. Mixed-input Gaussian process models have been used for emulating such models. The key in building this emulator is to accurately estimate the covariance between different categorical levels of the qualitative inputs. This problem is challenging when the number of categorical levels is large. We propose a sparse covariance estimation approach to estimating the covariance matrix with a large number of categorical levels for the mixed-input Gaussian process emulator. The effectiveness of this approach is illustrated with an application of IO operation modes in high performance computing systems.