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Author Song, J.W.; Wei, P.F.; Valdebenito, M.A.; Faes, M.; Beer, M. doi  openurl
  Title Data-driven and active learning of variance-based sensitivity indices with Bayesian probabilistic integration Type
  Year 2021 Publication Mechanical Systems and Signal Processing Abbreviated Journal Mech. Syst. Sig. Process.  
  Volume 163 Issue Pages 108106  
  Keywords Variance-based sensitivity; Gaussian process regression; Bayesian probabilistic integration; Data-driven; Adaptive experiment design; Posterior variance contribution  
  Abstract Variance-based sensitivity indices play an important role in scientific computation and data mining, thus the significance of developing numerical methods for efficient and reliable estimation of these sensitivity indices based on (expensive) computer simulators and/or data cannot be emphasized too much. In this article, the estimation of these sensitivity indices is treated as a statistical inference problem. Two principle lemmas are first proposed as rules of thumb for making the inference. After that, the posterior features for all the (partial) variance terms involved in the main and total effect indices are analytically derived (not in closed form) based on Bayesian Probabilistic Integration (BPI). This forms a data-driven method for estimating the sensitivity indices as well as the involved discretization errors. Further, to improve the efficiency of the developed method for expensive simulators, an acquisition function, named Posterior Variance Contribution (PVC), is utilized for realizing optimal designs of experiments, based on which an adaptive BPI method is established. The application of this framework is illustrated for the calculation of the main and total effect indices, but the proposed two principle lemmas also apply to the calculation of interaction effect indices. The performance of the development is demonstrated by an illustrative numerical example and three engineering benchmarks with finite element models.  
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  ISSN 0888-3270 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000687134700006 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1458  
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Author Valdebenito, M.A.; Wei, P.F.; Song, J.W.; Beer, M.; Broggi, M. doi  openurl
  Title Failure probability estimation of a class of series systems by multidomain Line Sampling Type
  Year 2021 Publication Reliability Engineering & System Safety Abbreviated Journal Reliab. Eng. Syst. Saf.  
  Volume 213 Issue Pages 107673  
  Keywords Line sampling; Multidomain; Linear performance function; Failure probability; Series system  
  Abstract This contribution proposes an approach for the assessment of the failure probability associated with a particular class of series systems. The type of systems considered involves components whose response is linear with respect to a number of Gaussian random variables. Component failure occurs whenever this response exceeds prescribed deterministic thresholds. We propose multidomain Line Sampling as an extension of the classical Line Sampling to work with a large number of components at once. By taking advantage of the linearity of the performance functions involved, multidomain Line Sampling explores the interactions that occur between failure domains associated with individual components in order to produce an estimate of the failure probability. The performance and effectiveness of multidomain Line Sampling is illustrated by means of two test problems and an application example, indicating that this technique is amenable for treating problems comprising both a large number of random variables and a large number of components.  
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  Series Volume Series Issue Edition  
  ISSN 0951-8320 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000663910500016 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1430  
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