Design of computer experiments: A review

被引:234
作者
Garud, Sushant S. [1 ]
Karimi, Iftekhar A. [1 ]
Kraft, Markus [2 ,3 ]
机构
[1] Natl Univ Singapore, Dept Chem & Biomol Engn, 4 Engn Dr 4, Singapore 117576, Singapore
[2] Univ Cambridge, Dept Chem Engn & Biotechnol, New Museums Site,Pembroke St, Cambridge CB2 3RA, England
[3] Nanyang Technol Univ, Sch Chem & Biomed Engn, 62 Nanyang Dr, Singapore 637459, Singapore
基金
新加坡国家研究基金会;
关键词
Design of experiments; Computer experiments; Adaptive sampling; Space-filling; Surrogate development; LATIN HYPERCUBE DESIGN; EFFICIENT SAMPLING TECHNIQUE; SURROGATE-BASED OPTIMIZATION; MONTE-CARLO METHODS; GLOBAL OPTIMIZATION; LOW-DISCREPANCY; FLEXIBILITY ANALYSIS; STRATEGY; MODEL; ALGORITHM;
D O I
10.1016/j.compchemeng.2017.05.010
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In this article, we present a detailed overview of the literature on the design of computer experiments. We classify the existing literature broadly into two categories, viz. static and adaptive design of experiments (DoE). We begin with the abundant literature available on static DoE, its chronological evolution, and its pros and cons. Our discussion naturally points to the challenges that are faced by the static techniques. The adaptive DoE techniques employ intelligent and iterative strategies to address these challenges by combining system knowledge with space-filling for sample placement. We critically analyze the adaptive DoE literature based on the key features of placement strategies. Our numerical and visual analyses of the static DoE techniques reveal the excellent performance of Sobol sampling (SOB3) for higher dimensions; and that of Hammersley (HAM) and Halton (HAL) sampling for lower dimensions. Finally, we provide several potential opportunities for the future modern DoE research. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:71 / 95
页数:25
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