Monte Carlo simulation using support vector machine and kernel density for failure probability estimation

Seunggyu Lee

Reliability Engineering and System Safety, 2021, vol. 209, issue C

Abstract: Monte Carlo simulation requires a large number of sampling points. In a Monte Carlo simulation, the performance function is calculated for all sampling points to determine the failure of the design. If the calculation of the performance function involves large numerical models, a tremendous numerical cost is inevitable. In this study, a support vector machine was applied as a metamodel of the performance function to overcome this drawback. Kernel density and a modified margin of the support vector machine were used for the active learning of the support vector machine. The proportion of the support vector machine's modified margin in the design space was applied as the criterion to end active learning. The proposed method is applied to some numerical examples and examined.

Keywords: Failure probability; Kernel density; Monte Carlo simulation; Support vector machine (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (22)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:209:y:2021:i:c:s0951832021000478

DOI: 10.1016/j.ress.2021.107481

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