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Surrogate model uncertainty quantification for active learning reliability analysis
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Surrogate model uncertainty quantification for active learning reliability analysis
Surrogate models offer an efficient approach to tackle the computationally intensive evaluation of performance functions in reliability analysis.Nevertheless,the approximations inher-ent in surrogate models necessitate the consideration of surrogate model uncertainty in estimating failure probabilities.This paper proposes a new reliability analysis method in which the uncertainty from the Kriging surrogate model is quantified simultaneously.This method treats surrogate model uncertainty as an independent entity,characterizing the estimation error of failure probabilities.Building upon the probabilistic classification function,a failure probability uncertainty is proposed by integrating the difference between the traditional indicator function and the probabilistic classi-fication function to quantify the impact of surrogate model uncertainty on failure probability esti-mation.Furthermore,the proposed uncertainty quantification method is applied to a newly designed reliability analysis approach termed SUQ-MCS,incorporating a proposed median approximation function for active learning.The proposed failure probability uncertainty serves as the stopping criterion of this framework.Through benchmarking,the effectiveness of the pro-posed uncertainty quantification method is validated.The empirical results present the competitive performance of the SUQ-MCS method relative to alternative approaches.
Reliability analysisKriging modelUncertainty quantificationActive learningMonte Carlo simulation
Yong PANG、Shuai ZHANG、Pengwei LIANG、Muchen WANG、Zhuangzhuang GONG、Xueguan SONG、Ziyun KAN
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State Key Laboratory of High-performance Precision Manufacturing,School of Mechanical Engineering,Dalian University of Technology,Dalian 116024,China
Reliability analysis Kriging model Uncertainty quantification Active learning Monte Carlo simulation
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