Low-Rank Tensor Approximations for Reliability Analysis

Abstract : Low-rank tensor approximations have recently emerged as a promising tool for efficiently building surrogates of computational models with high-dimensional input. In this paper, we shed light on issues related to their construction with greedy approaches and demonstrate that meta-models built with small experimental designs can be used to estimate tail probabilities with high accuracy.
Type de document :
Communication dans un congrès
12th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP12), Jul 2015, Vancouver, Canada
Liste complète des métadonnées

Littérature citée [6 références]  Voir  Masquer  Télécharger

https://hal.archives-ouvertes.fr/hal-01169564
Contributeur : Katerina Konakli <>
Soumis le : lundi 29 juin 2015 - 16:35:09
Dernière modification le : jeudi 11 janvier 2018 - 06:22:26
Document(s) archivé(s) le : mercredi 16 septembre 2015 - 06:27:01

Fichier

Paper_159_Konakli.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01169564, version 1

Collections

Citation

Katerina Konakli, Bruno Sudret. Low-Rank Tensor Approximations for Reliability Analysis. 12th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP12), Jul 2015, Vancouver, Canada. 〈hal-01169564〉

Partager

Métriques

Consultations de la notice

79

Téléchargements de fichiers

151