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Communication Dans Un Congrès Année : 2013

Modélisation de simulateurs industriels multifidélités : apport d'une approche bayésienne globale.

Céline Helbert

Résumé

Large industrial design codes are widely used today to represent complex physical systems. The understanding of the real phenomenon then passes through the study of these industrial simulators. The difficulty comes from the complexity of these codes and their high cost in computation time. In our work, we focus on the outputs of computer code that can be obtained with different levels of fidelity. Cheap simulations but potentially less representative can for example be obtained by degrading the numerical scheme. The objective of this paper is to present two different models that integrate multiple levels of fidelity of the studied response. We start by showing an example of how the information provided by degraded simulations can be integrated within the prior kriging and allow a significant improvement in the quality of the prediction. More generally, we show that the Bayesian approach is more relevant than the classical approach for uncertainty quantification, sensitivity analysis and global optimization. This point will be illustrated in three case studies from different industrial fields.Then we present the alternative model based on cokriging extensively developed in recent years. We suggest some ways to improve the model and we compare these two models on a petrol case study.
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Dates et versions

hal-00967489 , version 1 (28-03-2014)

Identifiants

  • HAL Id : hal-00967489 , version 1

Citer

Céline Helbert. Modélisation de simulateurs industriels multifidélités : apport d'une approche bayésienne globale.. Journée de Statistique de la SFDS, May 2013, France. pp.75. ⟨hal-00967489⟩
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