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.
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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⟩

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