Computer experiments with functional inputs and scalar outputs by a norm-based approach

Thomas Muehlenstaedt 1 Jana Fruth 2 Olivier Roustant 3, 4
1 TU - Technische Universität Dortmund [Dortmund]
2 OVGU - Otto-von-Guericke University [Magdeburg]
3 LIMOS - Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes
4 FAYOL-ENSMSE - Département Génie mathématique et industriel
Abstract : A framework for designing and analyzing computer experiments is presented, which is constructed for dealing with functional and scalar inputs and scalar outputs. For designing experiments with both functional and scalar inputs, a two-stage approach is suggested. The first stage consists of constructing a candidate set for each functional input. During the second stage, an optimal combination of the found candidate sets and a Latin hypercube for the scalar inputs is sought. The resulting designs can be considered to be generalizations of Latin hypercubes. Gaussian process models are explored as metamodel. The functional inputs are incorporated into the Kriging model by applying norms in order to define distances between two functional inputs. We propose the use of B-splines to make the calculation of these norms computationally feasible.
Mots-clés : space-filling design Gaussian process Maximin design
Type de document :
Article dans une revue
Statistics and Computing, Springer Verlag (Germany), 2017, 27 (4), pp.1083 - 1097. 〈10.1007/s11222-016-9672-z〉
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https://hal-emse.ccsd.cnrs.fr/emse-01506232
Contributeur : Florent Breuil <>
Soumis le : mercredi 12 avril 2017 - 09:52:53
Dernière modification le : lundi 28 mai 2018 - 13:38:02

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Thomas Muehlenstaedt, Jana Fruth, Olivier Roustant. Computer experiments with functional inputs and scalar outputs by a norm-based approach. Statistics and Computing, Springer Verlag (Germany), 2017, 27 (4), pp.1083 - 1097. 〈10.1007/s11222-016-9672-z〉. 〈emse-01506232〉

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