Rare event estimation using stochastic spectral embedding

Wagner, Paul-Remo; Marelli, Stefano; Papaioannou, Iason; Straub, Daniel; Sudret, Bruno

doi:10.1016/j.strusafe.2021.102179

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Cited by 6 publications

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“…Active learning is crucial in analysis of functions with discontinuity or singularity because it allows for the aforementioned exploration and exploitation necessary to find and resolve these features. For the sake of completeness, active learning for SSE has been proposed for reliability analysis [27], but it does not lead to an accurate approximation over the entire input random space. Its accuracy is limited to regions around the limit surface, which are important for an estimation of failure probability.…”

Section: Related Developmentsmentioning

confidence: 99%

Active Learning-based Domain Adaptive Localized Polynomial Chaos Expansion

Novák¹,

Shields²,

Sadílek³

et al. 2023

Preprint

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Effective construction of a general purpose surrogate model based on polynomial chaos expansion.• Novel method for sequential decomposition of the input random space and construction of local approximations.• Sequential domain decomposition and sample size extension based on an active learning methodology.• Active learning is represented by variance-based Θ criterion developed for polynomial chaos expansion.

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Section: Related Developmentsmentioning

confidence: 99%