Rare event estimation using stochastic spectral embedding

P. R. Wagner, S. Marelli, I. Papaioannou, D. Straub, B. Sudret

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Estimating the probability of rare failure events is an essential step in the reliability assessment of engineering systems. Computing this failure probability for complex non-linear systems is challenging, and has recently spurred the development of active-learning reliability methods. These methods approximate the limit-state function (LSF) using surrogate models trained with a sequentially enriched set of model evaluations. A recently proposed method called stochastic spectral embedding (SSE) aims to improve the local approximation accuracy of global, spectral surrogate modelling techniques by sequentially embedding local residual expansions in subdomains of the input space. In this work we apply SSE to the LSF, giving rise to a stochastic spectral embedding-based reliability (SSER) method. The resulting partition of the input space decomposes the failure probability into a set of easy-to-compute conditional failure probabilities. We propose a set of modifications that tailor the algorithm to efficiently solve rare event estimation problems. These modifications include specialized refinement domain selection, partitioning and enrichment strategies. We showcase the algorithm performance on four benchmark problems of various dimensionality and complexity in the LSF.

Original languageEnglish
Article number102179
JournalStructural Safety
Volume96
DOIs
StatePublished - May 2022

Keywords

  • Active learning
  • Rare event estimation
  • Reliability analysis
  • Sparse polynomial chaos expansions
  • Stochastic spectral embedding
  • Surrogate modelling
  • Uncertainty quantification

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