Volume 23, 2019
|Page(s)||245 - 270|
|Published online||03 May 2019|
Efficient sequential experimental design for surrogate modeling of nested codes
2 Laboratoire de Probabilités et Modèles Aléatoires, Université Paris Diderot, 75205 Paris Cedex 13, France.
3 Centre de Mathématiques Appliquées, Ecole Polytechnique, 91128 Palaiseau Cedex, France.
* Corresponding author: firstname.lastname@example.org
Accepted: 11 May 2018
In this paper we consider two nested computer codes, with the first code output as one of the second code inputs. A predictor of this nested code is obtained by coupling the Gaussian predictors of the two codes. This predictor is non Gaussian and computing its statistical moments can be cumbersome. Sequential designs aiming at improving the accuracy of the nested predictor are proposed. One of the criteria allows to choose which code to launch by taking into account the computational costs of the two codes. Finally, two adaptations of the non Gaussian predictor are proposed in order to compute the prediction mean and variance rapidly or exactly.
Mathematics Subject Classification: 62L05 / 60G15 / 62M20
Key words: Nested computer codes / surrogate model / Gaussian process / uncertainty quantification / Bayesian formalism / sequential design / computer experiments
© The authors. Published by EDP Sciences, SMAI 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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