Local Optimization of Black-Box Function with High or Infinite-Dimensional Inputs: Application to Nuclear SafetyReport as inadecuate




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1 MAP5 - MAP5 - Mathématiques Appliquées à Paris 5 2 CEREMADE - CEntre de REcherches en MAthématiques de la DEcision

Abstract : An adaptation of Response Surface Methodology RSM when the covariate is of high or infinite dimensional is proposed, providing a tool for black-box optimization in this context. We combine dimension reduction techniques with classical multivariate Design of Experiments DoE. We propose a method to generate experimental designs and extend usual properties orthogonality, rotatability,

. of multivariate designs to general high or infinite dimensional contexts. Different dimension reduction basis are considered including data-driven basis. The methodology is illustrated on simulated functional data and we discuss the choice of the different parameters, in particular the dimension of the approximation space. The method is finally applied to a problem of nuclear safety.

Keywords : Response Surface Methodology Design of Experiments Functional data analysis





Author: Angelina Roche -

Source: https://hal.archives-ouvertes.fr/



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