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Repulsive normalizing flow mixtures for adaptive importance sampling: reliability analysis of complex systems

Helal, Sara et al.
arXiv (All) · Papers
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computationmachine-learning
Accurate rare-event estimation can be computationally expensive. Classical adaptive importance sampling (IS) schemes often rely on restrictive proposal families and can struggle under multiple failure modes. We propose FAMIS, a flow-based multiple importance sampling (MIS) framework that learns a no
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