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

Bouhlel, Mohamed Amine and Bartoli, Nathalie and Otsmane, Abdelkader and Morlier, Joseph Improving kriging surrogates of high-dimensional design models by Partial Least Squares dimension reduction. (2016) Structural and Multidisciplinary Optimization, 53 (5). 935-952. ISSN 1615-147X

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Official URL: http://dx.doi.org/10.1007/s00158-015-1395-9


Engineering computer codes are often compu- tationally 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 param- eters of the model. We address this issue herein by con- structing a covariance kernel that depends on only a few parameters. The new kernel is constructed based on infor- mation 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.

Item Type:Article
HAL Id:hal-01598259
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Ecole nationale supérieure des Mines d'Albi-Carmaux - IMT Mines Albi (FRANCE)
Université de Toulouse > Institut National des Sciences Appliquées de Toulouse - INSA (FRANCE)
Université de Toulouse > Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE)
French research institutions > Office National d'Etudes et Recherches Aérospatiales - ONERA (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Other partners > SAFRAN (FRANCE)
Laboratory name:
Deposited On:29 Sep 2017 12:09

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