Low-rank tensor approximations for reliability analysis Konakli, Katerina; Sudret, Bruno
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.
Item Citations and Data
Attribution-NonCommercial-NoDerivs 2.5 Canada