Abstract
The design of experiments (DoE) is a key process in constructing a surrogate model: DoE methods are used to select the sample points at which simulations are to be conducted.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Box GE, Behnken DW (1960) Some new three level designs for the study of quantitative variables. 2:455–475
Box GE, Hunter JS (1961) The 2 k − p fractional factorial designs 3:311–351
Branchu S, Forbes RT, York P, Nyqvist H (1999) A central composite design to investigate the thermal stabilization of lysozyme. 16:702–708
Chen W (1995) A robust concept exploration method for configuring complex systems. Georgia Institute of Technology
Clarke SM, Griebsch JH, Simpson TW (2005) Analysis of support vector regression for approximation of complex engineering analyses. J Mech Des 127:1077–1087
Coello CAC (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41:113–127
Currin C, Mitchell T, Morris M, Ylvisaker D (1988) A Bayesian approach to the design and analysis of computer experiments. Oak Ridge National Lab, TN (USA)
Currin C, Mitchell T, Morris M, Ylvisaker D (1991) Bayesian prediction of deterministic functions, with applications to the design and analysis of computer experiments. J Am Stat Assoc 86:953–963
Evans M, Swartz T (2000) Approximating integrals via Monte Carlo and deterministic methods. OUP Oxford
Fang K-T, Lin DKJ (2003) Ch. 4. Uniform experimental designs and their applications in industry. 22:131–170
Fang K-T, Lin DK, Winker P, Zhang Y (2000) Uniform design: theory and application. 42:237–248
Garud SS, Karimi IA, Kraft M (2017) Design of computer experiments: a review. 106:71–95
Goel T, Haftka RT, Shyy W, Queipo NV (2007) Ensemble of surrogates. Struct Multidiscip Optim 33:199–216
Gratiet LL, Cannamela C (2012) Kriging-based sequential design strategies using fast cross-validation techniques with extensions to multi-fidelity computer codes. arXiv:1210.6187
Hedayat AS, Sloane NJA, Stufken J (2012) Orthogonal arrays: theory and applications. Springer Science & Business Media
Homaifar A, Qi CX, Lai SH (1994) Constrained optimization via genetic algorithms. Simulation 62:242–253
Jin R, Chen W, Sudjianto A (2002) On sequential sampling for global metamodeling in engineering design. In: ASME 2002 international design engineering technical conferences and computers and information in engineering conference, pp 539–548. American Society of Mechanical Engineers
Le Gratiet L, Cannamela C (2015) Cokriging-based sequential design strategies using fast cross-validation techniques for multi-fidelity computer codes. Technometrics 57:418–427
Lindley DV (1956) On a measure of the information provided by an experiment. Ann Math Stat 986–1005
McKay MD, Beckman RJ, Conover WJ (2000) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. 42:55–61
Mordechai S (2011) Applications of Monte Carlo method in science and engineering
Morris MD, Mitchell TJ (1995) Exploratory designs for computational experiments. 43:381–402
Myers RH, Montgomery DC, Anderson-Cook CM (2016) Response surface methodology: process and product optimization using designed experiments. Wiley
Owen AB (1992) Orthogonal arrays for computer experiments, integration and visualization. 439–452
Rasmussen CE (2004) Gaussian processes in machine learning. Advanced lectures on machine learning. Springer, pp 63–71
Robert C, Casella G (2013) Monte Carlo statistical methods. Springer Science & Business Media
Roberts GO, Gelman A, Gilks WR (1997) Weak convergence and optimal scaling of random walk Metropolis algorithms. Ann Appl Probab 7:110–120
Sacks J, Welch WJ, Mitchell TJ, Wynn HP (1989) Design and analysis of computer experiments. Stat Sci 409–423
Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423
Shewry MC, Wynn HP (1987) Maximum entropy sampling. J Appl Stat 14:165–170
Viana FA, Venter G, Balabanov V (2010) An algorithm for fast optimal Latin hypercube design of experiments. 82:135–156
Xiao M, Gao L, Shao X, Qiu H, Jiang P (2012) A generalised collaborative optimisation method and its combination with kriging metamodels for engineering design. J Eng Des 23:379–399
Zhou Q, Shao X, Jiang P, Zhou H, Cao L, Zhang L (2015a) A deterministic robust optimisation method under interval uncertainty based on the reverse model. J Eng Des 26:416–444
Zhou Q, Shao X, Jiang P, Zhou H, Shu L (2015b) An adaptive global variable fidelity metamodeling strategy using a support vector regression based scaling function. Simul Model Pract Theory 59:18–35
Zhou Q, Shao X, Jiang P, Gao Z, Zhou H, Shu L (2016) An active learning variable-fidelity metamodelling approach based on ensemble of metamodels and objective-oriented sequential sampling. J Eng Des 27:205–231
Zhou Q, Wang Y, Choi S-K, Jiang P, Shao X, Hu J (2017) A sequential multi-fidelity metamodeling approach for data regression. Knowl Based Syst 134:199–212
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Jiang, P., Zhou, Q., Shao, X. (2020). Sampling Approaches. In: Surrogate Model-Based Engineering Design and Optimization. Springer Tracts in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-0731-1_6
Download citation
DOI: https://doi.org/10.1007/978-981-15-0731-1_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0730-4
Online ISBN: 978-981-15-0731-1
eBook Packages: EngineeringEngineering (R0)