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A fast-convergence algorithm for reliability analysis based on the AK-MCS

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References

2002The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations4,821 cited
2011AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation2,048 cited
2007Kriging metamodeling in simulation: A review1,278 cited
2008Efficient Global Reliability Analysis for Nonlinear Implicit Performance Functions1,141 cited
2009An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis834 cited
2001Reliability analysis—a review and some perspectives775 cited
2012A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models692 cited
2013Metamodel-based importance sampling for structural reliability analysis535 cited
1988Adaptive sampling — an iterative fast Monte Carlo procedure464 cited
2016LIF: A new Kriging based learning function and its application to structural reliability analysis444 cited
2015A new learning function for Kriging and its applications to solve reliability problems in engineering400 cited
2015POLYNOMIAL-CHAOS-BASED KRIGING398 cited
2019REIF: A novel active-learning function toward adaptive Kriging surrogate models for structural reliability analysis297 cited
2016Rare Event Estimation Using Polynomial-Chaos Kriging

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279 cited

2014An improved adaptive kriging-based importance technique for sampling multiple failure regions of low probability278 cited

2006Chapter 18 Metamodel-Based Simulation Optimization265 cited

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