Niching Importance Sampling for Multi-modal Rare-event Simulation
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866910110120411136 |
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| author | Kinnear, Hugh J. DiazDelaO, F. A. |
| author_facet | Kinnear, Hugh J. DiazDelaO, F. A. |
| contents | This paper proposes niching importance sampling, a framework that combines concepts from reliability analysis, e.g. Markov chains, importance sampling, and relative cross entropy minimisation, with niching techniques from evolutionary multi-modal optimisation. The result is a highly robust estimator of the probability of failure, that can tackle sampling challenges posed by the underlying geometry of a reliability problem. Niching importance sampling is tested on a range of numerical examples and is shown to consistently avoid the degenerate behaviour observed for existing reliability methods on several multi-modal performance functions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_06417 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Niching Importance Sampling for Multi-modal Rare-event Simulation Kinnear, Hugh J. DiazDelaO, F. A. Computation This paper proposes niching importance sampling, a framework that combines concepts from reliability analysis, e.g. Markov chains, importance sampling, and relative cross entropy minimisation, with niching techniques from evolutionary multi-modal optimisation. The result is a highly robust estimator of the probability of failure, that can tackle sampling challenges posed by the underlying geometry of a reliability problem. Niching importance sampling is tested on a range of numerical examples and is shown to consistently avoid the degenerate behaviour observed for existing reliability methods on several multi-modal performance functions. |
| title | Niching Importance Sampling for Multi-modal Rare-event Simulation |
| topic | Computation |
| url | https://arxiv.org/abs/2604.06417 |