Low-Rank Tensor Approximations for Reliability Analysis
Résumé
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.
Origine : Fichiers produits par l'(les) auteur(s)
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