Active learning surrogate models for the conception of systems with multiple failure modes

Abstract : Due to performance and certification criteria, complex mechanical systems have to take into account several constraints, which can be associated with a series of performance functions. Different software are generally used to evaluate such functions, whose computational cost can vary a lot. In conception or reliability analysis, we thus are interested in the identification of the boundaries of the domain where all these constraints are satisfied, at the minimal total computational cost. To this end, the present work proposes an iterative method to maximize the knowledge about these limits while trying to minimize the required number of evaluations of each performance function. This method is based first on Gaussian process surrogate models that are defined on nested sub-spaces, and second, on an original selection criterion that takes into account the computational cost associated with each performance function. After presenting the theoretical basis of this approach, this paper compares its efficiency to alternative methods on an example.
Type de document :
Article dans une revue
Reliability Engineering and System Safety, Elsevier, 2016, 〈10.1016/j.ress.2015.12.017〉
Liste complète des métadonnées

Littérature citée [17 références]  Voir  Masquer  Télécharger

https://hal.archives-ouvertes.fr/hal-01266534
Contributeur : Guillaume Perrin <>
Soumis le : mardi 2 février 2016 - 21:10:46
Dernière modification le : lundi 15 octobre 2018 - 15:54:03
Document(s) archivé(s) le : samedi 12 novembre 2016 - 04:14:25

Fichier

PERRIN2015.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

CEA | DAM

Citation

G Perrin. Active learning surrogate models for the conception of systems with multiple failure modes. Reliability Engineering and System Safety, Elsevier, 2016, 〈10.1016/j.ress.2015.12.017〉. 〈hal-01266534〉

Partager

Métriques

Consultations de la notice

88

Téléchargements de fichiers

138