Space-filling designs for computer experiments: A review

Joseph, V. Roshan

doi:10.1080/08982112.2015.1100447

Title: Space-filling designs for computer experiments: A review

Journal Article · Fri Jan 29 00:00:00 EST 2016 · Quality Engineering

DOI:https://doi.org/10.1080/08982112.2015.1100447· OSTI ID:1405184

Joseph, V. Roshan ^[1]

Georgia Inst. of Technology, Atlanta, GA (United States)

Improving the quality of a product/process using a computer simulator is a much less expensive option than the real physical testing. However, simulation using computationally intensive computer models can be time consuming and therefore, directly doing the optimization on the computer simulator can be infeasible. Experimental design and statistical modeling techniques can be used for overcoming this problem. This article reviews experimental designs known as space-filling designs that are suitable for computer simulations. In the review, a special emphasis is given for a recently developed space-filling design called maximum projection design. Furthermore, its advantages are illustrated using a simulation conducted for optimizing a milling process.

View Accepted Manuscript (DOE)

Cite

Export

Save

Research Organization:: Georgia Institute of Technology, Atlanta, GA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Grant/Contract Number:: SC0010548

OSTI ID:: 1405184

Report Number(s):: DOE-GT-0010548-9; FG02-13ER26159

Journal Information:: Quality Engineering, Vol. 28, Issue 1; ISSN 0898-2112

Publisher:: American Society for Quality ControlCopyright Statement

Country of Publication:: United States

Language:: English

Citation Metrics:

Cited by: 93 works

Citation information provided by
Web of Science

References (26)

Quality Loss Functions for Nonnegative Variables and Their Applications Joseph, V. Roshan Journal of Quality Technology, Vol. 36, Issue 2 https://doi.org/10.1080/00224065.2004.11980260	journal	April 2004
Statistical Adjustments to Engineering Models Joseph, V. Roshan; Melkote, Shreyes N. Journal of Quality Technology, Vol. 41, Issue 4 https://doi.org/10.1080/00224065.2009.11917791	journal	October 2009
Bridge Designs for Modeling Systems With Low Noise Jones, Bradley; Silvestrini, Rachel T.; Montgomery, Douglas C. Technometrics, Vol. 57, Issue 2 https://doi.org/10.1080/00401706.2014.923788	journal	June 2014
Sequential Exploration of Complex Surfaces Using Minimum Energy Designs Joseph, V. Roshan; Dasgupta, Tirthankar; Tuo, Rui Technometrics, Vol. 57, Issue 1 https://doi.org/10.1080/00401706.2014.881749	journal	January 2015
Design and analysis of computer experiments Booker, Andrew 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization https://doi.org/10.2514/6.1998-4757	conference	September 1998
An Efficient Algorithm for Constructing Optimal Design of Computer Experiments Jin, Ruichen; Chen, Wei; Sudjianto, Agus ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Volume 2: 29th Design Automation Conference, Parts A and B https://doi.org/10.1115/detc2003/dac-48760	conference	June 2008
The Design and Analysis of Computer Experiments Santner, Thomas J.; Williams, Brian J.; Notz, William I. Springer Series in Statistics https://doi.org/10.1007/978-1-4757-3799-8	book	January 2003
Bayesian calibration of computer models Kennedy, Marc C.; O'Hagan, Anthony Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 63, Issue 3 https://doi.org/10.1111/1467-9868.00294	journal	August 2001
Post-Fisherian Experimentation: From Physical to Virtual Wu, C. F. Jeff Journal of the American Statistical Association, Vol. 110, Issue 510 https://doi.org/10.1080/01621459.2014.914441	journal	April 2015
Design and analysis of computer experiments Kuhnt, Sonja; Steinberg, David M. AStA Advances in Statistical Analysis, Vol. 94, Issue 4 https://doi.org/10.1007/s10182-010-0143-0	journal	December 2010
Generalized latin hypercube design for computer experiments Dette, Holger; Pepelyshev, Andrey Technische Universität Dortmund https://doi.org/10.17877/de290r-12659	text	January 2009
Multi-Layer Designs for Computer Experiments Ba, Shan; Joseph, V. Roshan Journal of the American Statistical Association, Vol. 106, Issue 495 https://doi.org/10.1198/jasa.2011.tm10229	journal	September 2011
Orthogonal Array-Based Latin Hypercubes Tang, Boxin Journal of the American Statistical Association, Vol. 88, Issue 424 https://doi.org/10.2307/2291282	journal	December 1993
The Design and Analysis of Computer Experiments Santner, Thomas J.; Williams, Brian J.; Notz, William I. Springer Series in Statistics https://doi.org/10.1007/978-1-4939-8847-1	book	January 2018
Generalized Latin Hypercube Design for Computer Experiments Dette, Holger; Pepelyshev, Andrey Technometrics, Vol. 52, Issue 4 https://doi.org/10.1198/TECH.2010.09157	journal	November 2010
An efficient algorithm for constructing optimal design of computer experiments Jin, Ruichen; Chen, Wei; Sudjianto, Agus Journal of Statistical Planning and Inference, Vol. 134, Issue 1 https://doi.org/10.1016/j.jspi.2004.02.014	journal	September 2005
Sliced space-filling designs Qian, P. Z. G.; Wu, C. F. J. Biometrika, Vol. 96, Issue 4 https://doi.org/10.1093/biomet/asp044	journal	October 2009
Exploratory designs for computational experiments Morris, Max D.; Mitchell, Toby J. Journal of Statistical Planning and Inference, Vol. 43, Issue 3 https://doi.org/10.1016/0378-3758(94)00035-T	journal	February 1995
Design and Analysis of Computer Experiments Sacks, Jerome; Welch, William J.; Mitchell, Toby J. Statistical Science, Vol. 4, Issue 4 https://doi.org/10.1214/ss/1177012413	journal	November 1989
Maximum entropy sampling Shewry, M. C.; Wynn, H. P. Journal of Applied Statistics, Vol. 14, Issue 2 https://doi.org/10.1080/02664768700000020	journal	January 1987
Minimax and maximin distance designs Johnson, M. E.; Moore, L. M.; Ylvisaker, D. Journal of Statistical Planning and Inference, Vol. 26, Issue 2 https://doi.org/10.1016/0378-3758(90)90122-B	journal	October 1990
Maximum projection designs for computer experiments Joseph, V. Roshan; Gul, Evren; Ba, Shan Biometrika, Vol. 102, Issue 2 https://doi.org/10.1093/biomet/asv002	journal	March 2015
Orthogonal Column Latin Hypercubes and Their Application in Computer Experiments Ye, Kenny Q. Journal of the American Statistical Association, Vol. 93, Issue 444 https://doi.org/10.1080/01621459.1998.10473803	journal	December 1998
Controlling Correlations in Latin Hypercube Samples Owen, Art B. Journal of the American Statistical Association, Vol. 89, Issue 428 https://doi.org/10.1080/01621459.1994.10476891	journal	December 1994
Orthogonal Array-Based Latin Hypercubes Tang, Boxin Journal of the American Statistical Association, Vol. 88, Issue 424 https://doi.org/10.1080/01621459.1993.10476423	journal	December 1993
Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code McKay, M. D.; Beckman, R. J.; Conover, W. J. Technometrics, Vol. 21, Issue 2 https://doi.org/10.1080/00401706.1979.10489755	journal	May 1979

Cited By (8)

Space-Filling Designs for Robustness Experiments Joseph, V. Roshan; Gu, Li; Ba, Shan Technometrics, Vol. 61, Issue 1 https://doi.org/10.1080/00401706.2018.1451390	journal	June 2018
A fast optimal Latin hypercube design method using an improved translational propagation algorithm Sun, Yibo; Meng, Xiuyun; Long, Teng Taylor & Francis https://doi.org/10.6084/m9.figshare.10033595.v1	text	January 2019
The Multiple-Update-Infill Sampling Method Using Minimum Energy Design for Sequential Surrogate Modeling Hwang, Yongmoon; Cha, Sang-Lyul; Kim, Sehoon Applied Sciences, Vol. 8, Issue 4 https://doi.org/10.3390/app8040481	journal	March 2018
A fast optimal Latin hypercube design method using an improved translational propagation algorithm Sun, Yibo; Meng, Xiuyun; Long, Teng Engineering Optimization https://doi.org/10.1080/0305215x.2019.1642881	journal	August 2019
A fast optimal Latin hypercube design method using an improved translational propagation algorithm Sun, Yibo; Meng, Xiuyun; Long, Teng Taylor & Francis https://doi.org/10.6084/m9.figshare.10033595	text	January 2019
Developing a space-filling mixture experiment design when the components are subject to linear and nonlinear constraints Piepel, Greg F.; Stanfill, Bryan A.; Cooley, Scott K. Quality Engineering, Vol. 31, Issue 3 https://doi.org/10.1080/08982112.2018.1517887	journal	December 2018
Design and analysis of computer experiments with quantitative and qualitative inputs: A selective review Kang, Xiaoning; Deng, Xinwei WIREs Data Mining and Knowledge Discovery, Vol. 10, Issue 3 https://doi.org/10.1002/widm.1358	journal	February 2020
Space-Filling Designs for Robustness Experiments Joseph, V. Roshan; Gu, Li; Ba, Shan arXiv https://doi.org/10.48550/arxiv.1712.09074	text	January 2017

Similar Records

Applied & Computational MathematicsChallenges for the Design and Control of Dynamic Energy Systems

Technical Report · Thu Mar 10 00:00:00 EST 2011 · OSTI ID:1405184

Brown, D L; Burns, J A; Collis, S; +6 more

Data Locality Enhancement of Dynamic Simulations for Exascale Computing (Final Report)

Technical Report · Fri Nov 29 00:00:00 EST 2019 · OSTI ID:1405184

Shen, Xipeng

Adaptive Sampling Algorithms for Probabilistic Risk Assessment of Nuclear Simulations

Conference · Sun Sep 01 00:00:00 EDT 2013 · OSTI ID:1405184

Mandelli, Diego; Maljovec, Dan; Wang, Bei; +2 more

Related Subjects

97 MATHEMATICS AND COMPUTING

Space-filling designs for computer experiments: A review Joseph, V. Roshan https://doi.org/10.6084/m9.figshare.2068545 The current record is supplemented by this dataset	dataset	January 2016
Space-filling designs for computer experiments: A review Joseph, V. Roshan https://doi.org/10.6084/m9.figshare.2068545.v1 The current record is supplemented by this dataset	dataset	January 2016

Title: Space-filling designs for computer experiments: A review

Citation Formats

References (26)

Cited By (8)

Linked Research (2)

Similar Records

Related Subjects