Efficient sequential experimental design for surrogate modeling of nested codes

Sophie Marque-Pucheu (1) , Guillaume Perrin (2) , Josselin Garnier

1 LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation
2 LESIA (UMR_8109) - Laboratoire d'études spatiales et d'instrumentation en astrophysique

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.

Mots clés: Nested computer codes; surrogate model; Gaussian process; uncertainty quantification; Bayesian formalism; sequential design; computer experiments

Domaines: Mathématiques [math]

Sophie Marque-Pucheu, Guillaume Perrin, Josselin Garnier. Efficient sequential experimental design for surrogate modeling of nested codes. ESAIM: Probability and Statistics, 2019, 23, pp.245-270. ⟨10.1051/ps/2018011⟩. ⟨hal-02925329⟩