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Adaptive learning function selection for Kriging-assisted structural reliability analysis
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DOI：10.1016/j.apm.2025.116614.png)
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Abstract
En 中文
• Dynamically selecting learning function for structural reliability. • Extensible framework, supports more learning functions or surrogate models. • Unified reward merges limit-state distance and uncertainty for explore-exploit. • Fewer iterations and evaluations, stable Pf on benchmarks and cases. • End-to-end timing shows lower total cost, cales to high dimensions.
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