Assessing the value of another cycle in Gaussian process surrogate-based optimization

Structural and Multidisciplinary Optimization

Volumn 39, Issue 5, 2009, Pages 459-475

  Queipo, Nestor V ^a   Verde, Alexander ^a   Pintos, Salvador ^a   Haftka, Raphael T ^b  

^a UNIVERSIDAD DEL ZULIA   (Venezuela)

^b UNIVERSITY OF FLORIDA   (United States)

Author keywords

Gaussian process; Short cycle optimization; Surrogate based optimization

Indexed keywords

COMPLEX ENGINEERING SYSTEM; COST CONSTRAINTS; COVARIANCE MODELS; DESIGN CYCLE; DESIGN OPTIMIZATION; ENGINEERING DESIGN; EXACT ANALYSIS; GAUSSIAN PROCESS; GAUSSIAN PROCESSES; INPUT/OUTPUT DATUM; NUMBER OF CYCLES; OIL INDUSTRIES; POLYMER FLOODING; SHORT CYCLE; SHORT CYCLE OPTIMIZATION; SURROGATE MODEL; SURROGATE-BASED OPTIMIZATION; TEST FUNCTIONS;

COVARIANCE MATRIX; DESIGN; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); PETROLEUM REFINERIES; PETROLEUM RESERVOIR ENGINEERING; PETROLEUM RESERVOIRS; SURFACE ACTIVE AGENTS;

OPTIMIZATION;

EID: 70350228440     PISSN: 1615147X     EISSN: 16151488     Source Type: Journal    
DOI: 10.1007/s00158-008-0346-0     Document Type: Article

References (41)

1. N Alexandrov 1998 A trust region framework for managing the use of approximation models in optimization Struct Optim 15 1 16 23 10.1007/BF01197433
2. DW Apley J Liu W Chen 2006 Understanding the effects of model uncertainty in robust design with computer experiments J Mech Des 128 945 10.1115/1.2204974 July (Pubitemid 44123184)
3. Chiles JP, Delfiner P (1999) Geostatistics: modeling spatial uncertainty. Wiley-Interscience, Hoboken
4. Cressie N (1993) Statistics for spatial data. Wiley, New York
5. Dosher TM, Wise FA (1976) Enhanced oil recovery potential. An estimate. Paper SPE 5800, J Petroleum Technology, pp 575
6. Eldred MS, Giunta AA, Wojtkiewicz SF Jr., Trucano TG (2002) Formulations for surrogate-based optimization under uncertainty. In: 9th AIAA/ISSMO Symposium on multidisciplinary analysis and optimization, Atlanta, GA, AIAA-2002-5585
7. Engelsen S, Lake LW, Lin EC, Ohno T, Pope GA, Camilleri D, Sepehrnoori K (1987) Description of an improved compositional micellar/polymer simulator. Paper SPE 13967, SPE Reservoir Engineering, pp 427-432
8. Forrester A, Jones D (2008) Global optimization of deceptive functions with spare sampling. In: 12th AIAA/ISSMO multidisciplinary analysis and optimization conference, Victoria, British Columbia, Canada, 10-12 September
9. A Genz 1993 A comparison of methods for numerical computation of multivariate normal probabilities Comput Sci Stat 25 400 405
10. Ginsbourger D, Le Riche R, Carraro L (2007) Multi-points criterion for deterministic parallel global optimization based on Kriging. In: Intl. Conf. on Nonconvex Programming, NCP07, Rouen, France, December
11. Hollingsworth P, Mavris D (2003) A technique for use of Gaussian processes in advanced meta-modeling. Proceedings of the SAE Aerospace Congress and Exhibition, Montreal, Canada
12. D Huang TT Allen WI Notz N Zeng 2006 Global optimization of stochastic black-box systems via sequential kriging meta-models J Glob Optim 34 441 466 1098.90097 10.1007/s10898-005-2454-3 2222283 (Pubitemid 43352177)
13. D Jones 2001 A taxonomy of global optimization methods based on response surfaces J Glob Optim 21 345 383 01845262 10.1023/A:1012771025575 (Pubitemid 33672374)
14. DR Jones CD Perttunen BE Stuckman 1993 Lipschitzian optimization without the Lipschitz constant J Optim Theory Appl 79 1 157 181 0796.49032 10.1007/BF00941892 1246501
15. DR Jones M Schonlau WJ Welch 1998 Efficient global optimization of expensive black-box functions J Glob Optim 13 455 492 0917.90270 10.1023/A:1008306431147 1673460
16. Kageyama Y, Yu Q, Shiratori M (2001) Robust and optimal parameter design using SDSS and reliability design and application to hydraulic servo valve. In: Hernandez S, Brebbia CA (eds) Computer aided optimum design of structures VII. MIT Press, Southampton, pp 13-22
17. Koch PN, Gu L (2001) Addressing uncertainty using the iSIGHT probabilistic design environment. First Annual Probabilistic Methods Conference, Newport Beach
18. Lake L (1989) Enhanced oil recovery. Prentice Hall, Englewood Cliffs, p 408
19. Li W, Padula S (2004) Approximation methods for conceptual design of complex systems. In: 11th international conference on approximation theory
20. Lophaven S, Nielsen H, Sondergaard J (2002) DACE: A Matlab kriging toolbox, version 2.0. Informatics and Mathematical Modeling, Technical University of Denmark. http://www.imm.dtu.dk/hbn/dace/
21. D Lowe 1995 Similarity metric learning for a variable kernel classifier Neural Comput 7 72 85 10.1162/neco.1995.7.1.72
22. Neal R (1996) Bayesian learning for neural networks. Lecture notes in statistics, vol 118. Springer, New York
23. Padula SL, Korte JJ, Dunn HJ, Salas A (1999) Multidisciplinary optimization branch experience using iSIGHT software. NASA/TM-1999-209714, Langley Research Center, Hampton
24. Poggio T, Girosi F (1989) A theory of networks for approximation and learning. Technical report A.I., 1140, M.I.T
25. Ponweiser W, Wagner T, Vincze M (2008) Clustered multiple generalized expected improvement: a novel infill sampling criterion for surrogate models. In: Proceedings of the IEEE congress on evolutionary computation (CEC 2008), June 1-6, Hong Kong, IEEE, pp 3514-3521. ISBN 978-1-4244-1823-7
26. Pope GA, Nelson RC (1978) A chemical flooding compositional simulator. Paper SPE 6725, Society of Petroleum Engineers
27. N Queipo R Haftka W Shyy T Goel R Vaidyanathan PK Tucker 2005 Surrogate-based analysis and optimization J Prog Aerosp Sci 41 1 28 10.1016/j.paerosci.2005.02.001 (Pubitemid 40537187)
28. Rao CR (2002) Linear statistical inference and its applications, 2nd edn. Wiley, New York, pp 516-533
29. Rasmussen C (1996) Evaluation of Gaussian processes and other methods for non-linear regression. PhD Thesis. Department of Computer Science, University of Toronto
30. JF Rodriguez JE Renaud LT Watson 1998 Trust region augmented Lagrangian methods for sequential response surface approximation and optimization ASME J Mech Design 120 58 66 10.1115/1.2826677
31. J Sacks W Welch T Mitchell H Wynn 1989 Design and analysis of computer experiments Stat Sci 4 409 423 0955.62619 10.1214/ss/1177012413 1041765
32. MJ Sasena P Papalambros P Goovaerts 2002 Exploration of metamodeling sampling criteria for constrained global optimization Eng Optim 34 3 263 278 10.1080/03052150211751 January (Pubitemid 35263141)
33. Simpson TW, Booker AJ, Ghosh D, Giunta AA, Koch PN, Yang R-J (2002) Approximation methods in multidisciplinary analysis and optimization: a panel discussion. In: 3rd ISSMO/AIAA internet conference on approximations in optimization
34. A Sobester SJ Leary AJ Keane 2005 On the design of optimization strategies based on global response surface approximation models J Glob Optim 33 31 59 1137.90743 10.1007/s10898-004-6733-1 2190713
35. A Vecchia 1998 Estimation and model identification for continuous spatial processes Statist Soc Ser B 50 297 312 964183
36. Wahba G (1990) Spline models for observational data. In: SIAM, CBMS-NSF regional conference series in Applied Mathematics
37. GG Wang S Shan 2007 Review of metamodeling techniques in support of engineering design optimization ASME Trans J Mech Des 129 370 380 10.1115/1.2429697 April 2007 (Pubitemid 46736217)
38. Williams C (1998) Prediction with Gaussian processes: from linear regression to linear prediction and beyond. Learning in graphical models. Kluwer Academic, Dordrecht, pp 599-621
39. Williams C (2002) Gaussian processes. In: Arbib M (ed) The handbook of brain theory and neural networks, 2nd edn. MIT Press, Cambridge
40. Wojtkiewicz SF, Eldred MS, Field RV Jr, Urbina A, Red-Horse JR (2001) A toolkit for uncertainty quantification in large computational engineering models. In: Proceedings of the 42nd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics, and materials conference. Seattle, WA, AIAA-2001-1455
41. L Zerpa N Queipo S Pintos J Salager 2005 An optimization methodology of alkaline-surfactant-polymer flooding processes using field scale numerical simulation and multiple surrogates J Pet Sci Eng 47 197 208 10.1016/j.petrol. 2005.03.002 (Pubitemid 40829362)