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Surrogate model uncertainty quantification for active learning reliability analysis
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DOI：10.1016/j.cja.2024.08.055.png)
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Abstract
En 中文
Surrogate models offer an efficient approach to tackle the computationally intensive evaluation of performance functions in reliability analysis. Nevertheless, the approximations inherent 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 classification function to quantify the impact of surrogate model uncertainty on failure probability estimation. 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 proposed uncertainty quantification method is validated. The empirical results present the competitive performance of the SUQ-MCS method relative to alternative approaches. (c) 2024 Production and hosting by Elsevier Ltd. on behalf of Chinese Society of Aeronautics and Astronautics. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
Keywords:
Reliability analysis
Kriging model
Uncertainty quantification
Active learning
Monte Carlo simulation
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