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Practical enhancement of failure-probability estimation using probability density-driven active learning
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DOI：10.1016/j.probengmech.2025.103871.png)
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Abstract
En 中文
• Proposes a new T-learning function that integrates prior probability into active learning. • Demonstrates superior robustness to random initial conditions compared to existing methods. • Evaluates the reliability of stopping criteria under single-evaluation settings.
Keywords:
Surrogate model
Gaussian process regression
Reliability analysis
Learning function
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