Abstract:Estimating the probabilities of rare failure events is a key challenge in the reliability analysis of physical systems. Subset simulation (SS) is a very popular adaptive Monte Carlo method for this problem. In SS, the small failure probability is evaluated as a product of larger conditional probabilities by iteratively sampling a sequence of nested sub-domains of the parameter space, encompassing the target failure domain of interest, using Markov chain Monte Carlo methods. For failure domains with multiple modes, the Markov chain samples used to explore the intermediate levels of SS can be trapped in a confined region of the input parameter space, leading to inaccurate failure probability estimates. In this contribution, we propose the directional subset simulation (dSS) method for this problem, which uses concepts from directional sampling to informedly propagate samples towards failure. This is accomplished through a novel selection of the intermediate failure domains, which preserves samples in several directions in the parameter space in each intermediate level. The merits of the dSS method are illustrated through a selection of numerical examples.
| Subjects: | Computation (stat.CO) |
| Cite as: | arXiv:2605.23631 [stat.CO] |
| (or arXiv:2605.23631v1 [stat.CO] for this version) | |
| https://doi.org/10.48550/arXiv.2605.23631 arXiv-issued DOI via DataCite (pending registration) |
From: Julien Bect [view email] [via CCSD proxy]
[v1]
Fri, 22 May 2026 13:41:31 UTC (2,432 KB)
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