Journal
TECHNOMETRICS
Volume 65, Issue 2, Pages 220-230Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/00401706.2022.2141897
Keywords
Experimental design; Morris screening; Sensitivity analysis; Sobol' indices
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This article explores the difficult problem of identifying important factors from a large number of potentially important factors in a highly nonlinear and computationally expensive black box model. By establishing a connection between Morris screening and Sobol' design, an improved design called MOFAT is developed for screening, along with efficient methods for constructing MOFAT designs with a large number of factors.
Identifying important factors from a large number of potentially important factors of a highly nonlinear and computationally expensive black box model is a difficult problem. Morris screening and Sobol' design are two commonly used model-free methods for doing this. In this article, we establish a connection between these two seemingly different methods in terms of their underlying experimental design structure and further exploit this connection to develop an improved design for screening called Maximum One-Factor-At-A-Time (MOFAT) design. We also develop efficient methods for constructing MOFAT designs with a large number of factors. Several examples are presented to demonstrate the advantages of MOFAT designs compared to Morris screening and Sobol' design methods.
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