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Author (up) Dang, C.; Wei, P.F.; Faes, M.G.R.; Valdebenito, M.A.; Beer, M. doi  openurl
  Title Parallel adaptive Bayesian quadrature for rare event estimation Type
  Year 2022 Publication Reliability Engineering & System Safety Abbreviated Journal Reliab. Eng. Syst. Saf.  
  Volume 225 Issue Pages 108621  
  Keywords Reliability analysis; Gaussian process; Numerical uncertainty; Bayesian quadrature; Parallel computing  
  Abstract Various numerical methods have been extensively studied and used for reliability analysis over the past several decades. However, how to understand the effect of numerical uncertainty (i.e., numerical error due to the discretization of the performance function) on the failure probability is still a challenging issue. The active learning probabilistic integration (ALPI) method offers a principled approach to quantify, propagate and reduce the numerical uncertainty via computation within a Bayesian framework, which has not been fully investigated in context of probabilistic reliability analysis. In this study, a novel method termed `Parallel Adaptive Bayesian Quadrature' (PABQ) is proposed on the theoretical basis of ALPI, and is aimed at broadening its scope of application. First, the Monte Carlo method used in ALPI is replaced with an importance ball sampling technique so as to reduce the sample size that is needed for rare failure event estimation. Second, a multi-point selection criterion is proposed to enable parallel distributed processing. Four numerical examples are studied to demonstrate the effectiveness and efficiency of the proposed method. It is shown that PABQ can effectively assess small failure probabilities (e.g., as low as 10(-7)) with a minimum number of iterations by taking advantage of parallel computing.  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0951-8320 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000809316300008 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1607  
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