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Autori principali: Kinnear, Hugh J., DiazDelaO, F. A.
Natura: Preprint
Pubblicazione: 2026
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Accesso online:https://arxiv.org/abs/2604.06417
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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