Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Structural and Multidisciplinary Optimization
Erschienen in: Structural and Multidisciplinary Optimization 5/2016

14.01.2016 | REVIEW ARTICLE

Improving kriging surrogates of high-dimensional design models by Partial Least Squares dimension reduction

verfasst von: Mohamed Amine Bouhlel, Nathalie Bartoli, Abdelkader Otsmane, Joseph Morlier

Erschienen in: Structural and Multidisciplinary Optimization | Ausgabe 5/2016

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Engineering computer codes are often computationally expensive. To lighten this load, we exploit new covariance kernels to replace computationally expensive codes with surrogate models. For input spaces with large dimensions, using the kriging model in the standard way is computationally expensive because a large covariance matrix must be inverted several times to estimate the parameters of the model. We address this issue herein by constructing a covariance kernel that depends on only a few parameters. The new kernel is constructed based on information obtained from the Partial Least Squares method. Promising results are obtained for numerical examples with up to 100 dimensions, and significant computational gain is obtained while maintaining sufficient accuracy.
Nächster Artikel Sensitivity reanalysis of static displacement using Taylor series expansion and combined approximate method

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Anhänge
Nur mit Berechtigung zugänglich
Literatur
Alberto P, González F (2012) Partial Least Squares regression on symmetric positive-definite matrices. Rev Col Estad 36(1):177–192MathSciNetMATH
Bachoc F (2013) Cross Validation and Maximum Likelihood estimation of hyper-parameters of Gaussian processes with model misspecification. Comput Stat Data Anal 66:55–69MathSciNetCrossRef
Bishop CM (2007) Pattern recognition and machine learning (information science and statistics). Springer
Braham H, Ben Jemaa S, Sayrac B, Fort G, Moulines E (2014) Low complexity spatial interpolation for cellular coverage analysis. In: 2014 12th international symposium on modeling and optimization in mobile, ad hoc, and wireless networks (WiOpt). IEEE, pp 188–195
Buhmann MD (2003) Radial basis functions: theory and implementations, vol 12. Cambridge University Press, CambridgeCrossRefMATH
Damianou A, Lawrence ND (2013) Deep gaussian processes. In: Proceedings of the sixteenth international conference on artificial intelligence and statistics, AISTATS 2013, Scottsdale, pp 207–215
Durrande N (2011) Covariance kernels for simplified and interpretable modeling. A functional and probabilistic approach. Theses, Ecole Nationale Supérieure des Mines de saint-Etienne
Durrande N, Ginsbourger D, Roustant O (2012) Additive covariance kernels for high-dimensional gaussian process modeling. Ann Fac Sci Toulouse Math 21(3):481–499MathSciNetCrossRefMATH
Forrester A, Sobester A, Keane A (2008) Engineering design via surrogate modelling: a practical guide. Wiley, New YorkCrossRef
Frank IE, Friedman JH (1993) A statistical view of some chemometrics regression tools. Technometrics 35:109–148CrossRefMATH
Goovaerts P (1997) Geostatistics for natural resources evaluation (applied geostatistics). Oxford University Press, New York
Haykin S (1998) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall PTR, Upper Saddle River
Hensman J, Fusi N, Lawrence ND (2013) Gaussian processes for big data. In: Proceedings of the twenty-ninth conference on uncertainty in artificial intelligence, Bellevue, p 2013
Jones DR, Schonlau M, Welch WJ (1998) Efficient global optimization of expensive black-box functions. J Glob Optim 13(4):455–492MathSciNetCrossRefMATH
Lanczos C (1950) An iteration method for the solution of the eigenvalue problem of linear differential and integral operators. J Res Natl Bur Stand 45(4):255–282MathSciNetCrossRef
Liem RP, Martins JRRA (2014) Surrogate models and mixtures of experts in aerodynamic performance prediction for mission analysis. In: 15th AIAA/ISSMO multidisciplinary analysis and optimization conference, Atlanta, GA, AIAA-2014-2301
Manne R (1987) Analysis of two Partial-Least-Squares algorithms for multivariate calibration. Chemom Intell Lab Syst 2(1–3):187–197CrossRef
Mera NS (2007) Efficient optimization processes using kriging approximation models in electrical impedance tomography. Int J Numer Methods Eng 69(1):202–220MathSciNetCrossRefMATH
Michalewicz Z, Schoenauer M (1996) Evolutionary algorithms for constrained parameter optimization problems. Evol Comput 4: 1–32CrossRef
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830MathSciNetMATH
Picheny V, Ginsbourger D, Roustant O, Haftka RT, Kim NH (2010) Adaptive designs of experiments for accurate approximation of a target region. J Mech Des 132(7):071008CrossRef
Powell MJ (1994) A direct search optimization method that models the objective and constraint functions by linear interpolation. In: Advances in optimization and numerical analysis. Springer, pp 51–67
Rasmussen C, Williams C (2006) Gaussian processes for machine learning. Adaptive computation and machine learning. MIT Press, CambridgeMATH
Regis R, Shoemaker C (2013) Combining radial basis function surrogates and dynamic coordinate search in high-dimensional expensive black-box optimization. Eng Optim 45(5):529–555MathSciNetCrossRef
Roustant O, Ginsbourger D, Deville Y (2012) DiceKriging, DiceOptim: two R packages for the analysis of computer experiments by kriging-based metamodeling and optimization. J Stat Softw 51(1):1–55CrossRef
Sakata S, Ashida F, Zako M (2004) An efficient algorithm for Kriging approximation and optimization with large-scale sampling data. Comput Methods Appl Mech Eng 193(3):385–404CrossRefMATH
Sasena M (2002) Flexibility and efficiency enhancements for constrained global design optimization with Kriging approximations. PhD thesis, University of Michigan
Schonlau M (1998) Computer experiments and global optimization. PhD thesis, University of Waterloo
Wahba G (1990) Spline models for observational data, CBMS-NSF regional conference series in applied mathematics, vol 59. Society for Industrial and Applied Mathematics (SIAM), Philadelphia
Wahba G, Craven P (1978) Smoothing noisy data with spline functions. Estimating the correct degree of smoothing by the method of generalized cross-validation. Numer Math 31:377–404MathSciNetCrossRefMATH
Zimmerman DL, Homer KE (1991) A network design criterion for estimating selected attributes of the semivariogram. Environmetrics 2(4):425–441CrossRef
Metadaten
Titel
Improving kriging surrogates of high-dimensional design models by Partial Least Squares dimension reduction
verfasst von
Mohamed Amine Bouhlel
Nathalie Bartoli
Abdelkader Otsmane
Joseph Morlier
Publikationsdatum
14.01.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Structural and Multidisciplinary Optimization / Ausgabe 5/2016
Print ISSN: 1615-147X
Elektronische ISSN: 1615-1488
DOI
https://doi.org/10.1007/s00158-015-1395-9

Weitere Artikel der Ausgabe 5/2016

Structural and Multidisciplinary Optimization 5/2016 Zur Ausgabe

INDUSTRIAL APPLICATION

Multidisciplinary design and optimization of the twin-web turbine disk

RESEARCH PAPER

Ensemble of metamodels: the augmented least squares approach

BRIEF NOTE

Multiplicity of the maximum eigenvalue in structural optimization problems

RESEARCH PAPER

Structural topology optimization with design-dependent pressure loads

RESEARCH PAPER

Damage approach: A new method for topology optimization with local stress constraints

RESEARCH PAPER

Reliability based optimization in aeroelastic stability problems using polynomial chaos based metamodels

Premium Partner

    Marktübersichten

    Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen. 

    Zur Marktübersicht
    Bildnachweise