Journal Article10.1016/J.RESS.2020.106857
A novel learning function based on Kriging for reliability analysis
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TL;DR: A new learning function called Folded Normal based Expected Improvement Function (FNEIF) is proposed to efficiently estimate the failure probability of the surrogate model for reliability analysis.
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About: This article is published in Reliability Engineering & System Safety. The article was published on 01 Jun 2020. The article focuses on the topics: Folded normal distribution & Surrogate model.
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Citations
A system active learning Kriging method for system reliability-based design optimization with a multiple response model
Mi Xiao,Jinhao Zhang,Liang Gao +2 more
TL;DR: The results indicate that SALK can locally approximate the limit-state surfaces around the finalSRBDO solution and efficiently reduce the computational cost on the refinement of the region far from the final SR BDO solution.
126
A stochastic process discretization method combing active learning Kriging model for efficient time-variant reliability analysis
TL;DR: In this study, three numerical analysis examples and one engineering design example are presented to demonstrate the effectiveness of the proposed Kriging-assisted time-variant reliability analysis method based upon stochastic process discretization.
72
AKSE: A novel adaptive Kriging method combining sampling region scheme and error-based stopping criterion for structural reliability analysis
TL;DR: In this article , an adaptive Kriging-based method is proposed for the estimation of failure probability with high accuracy, where a small set of initial design of experiments (DoE) is constructed and iteratively refined by adding judiciously selected sample points to the DoE.
66
System reliability analysis based on dependent Kriging predictions and parallel learning strategy
TL;DR: In this paper , a new learning function with a parallel processing strategy is proposed for selecting new training samples for complex systems, which combines dependent Kriging predictions and parallel learning strategy to further improve the computational efficiency.
63
A novel learning function for adaptive surrogate-model-based reliability evaluation.
Debiao Meng,Hongtao Wang +1 more
- 08 Jan 2024
TL;DR: A novel learning function for adaptive surrogate-model-based reliability evaluation improves computational efficiency and accuracy without relying on the prediction variance provided by the Kriging model.
59
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