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Bayesian probability density evolution method for reliability analysis with stepwise uncertainty reduction
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DOI：10.1016/j.ress.2026.112967.png)
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Abstract
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• An active learning function seeks to minimize the variance of failure probability. • Discrete importance sampling is devised for efficiently computing the learning function. • Multi-point enrichment is conducted via a stepwise maximization strategy. • Calibrated epistemic uncertainty resolves overestimation issues of upper-bounds.
Keywords:
Expected variance reduction
Probability density evolution method
Kriging
Discrete importance sampling
Reliability analysis
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