Formulates active learning sample acquisition for surrogate model-based reliability analysis as multi-objective optimization yielding a Pareto set, with adaptive selection rules that show robust performance across tested limit-state functions.
Rackwitz, Reliability analysis — A review and some perspectives, Structural Safety 23 (4) (2001) 365–395
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Balancing the exploration-exploitation trade-off in active learning for surrogate model-based reliability analysis via multi-objective optimization
Formulates active learning sample acquisition for surrogate model-based reliability analysis as multi-objective optimization yielding a Pareto set, with adaptive selection rules that show robust performance across tested limit-state functions.