How Adaptively Constructed Reduced Order Models Can Benefit Sampling-Based Methodsfor Reliability Analyses

Abstract : Many sampling-based approaches are currently available for calculating the reliability of a design. The most efficient methods can achieve reductions in the computational cost by one to several orders of magnitude compared to the basic Monte Carlo method. This paper is specifically targeted at sampling-based approaches for reliability analysis, in which the samples represent calls to expensive finite element models. The aim of this paper is to illustrate how these methods can further benefit from reduced order modeling to achieve drastic additional computational cost reductions, in cases where the reliability analysis is carried out on finite element models. Standard Monte Carlo, importance sampling, separable Monte Carlo and a combined importance separable Monte Carlo approach are presented and coupled with reduced order modeling. An adaptive construction of the reduced basis models is proposed. The various approaches are compared on a thermal reliability design problem, where the coupling with the adaptively constructed reduced order models is shown to further increase the computational efficiency by up to a factor of six.
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Contributor : Pierre Naegelen <>
Submitted on : Tuesday, December 11, 2018 - 9:16:45 AM
Last modification on : Friday, April 12, 2019 - 4:23:34 PM

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  • HAL Id : hal-01950773, version 1

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Christian Gogu, Anirban Chaudhuri, Christian Bes. How Adaptively Constructed Reduced Order Models Can Benefit Sampling-Based Methodsfor Reliability Analyses. International Journal of Reliability, Quality and Safety Engineering, 2016, 23 (05), pp.1650019. ⟨hal-01950773⟩

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