A new surrogate refinement strategy guided by adaptive importance sampling proposals achieves comparable rare-event accuracy to full-model methods while using far fewer high-fidelity PDE evaluations in up to 100 dimensions.
A new active learning PGGR 25 method based on the learning function u of the ak-mcs reliability analysis method
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Proposal-Guided Greedy Surrogate Refinement for PDE-Driven High-Dimensional Rare-Event Estimation
A new surrogate refinement strategy guided by adaptive importance sampling proposals achieves comparable rare-event accuracy to full-model methods while using far fewer high-fidelity PDE evaluations in up to 100 dimensions.