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A physics and data co-driven surrogate modeling method for high-dimensional rare event simulation
16 references·2 citing·38 second hop
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References

  • 2001Estimation of small failure probabilities in high dimensions by subset simulation2,419 cited
  • 2011AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation2,048 cited
  • 2011Comparison of Values of Pearson's and Spearman's Correlation Coefficients on the Same Sets of Data1,987 cited
  • 1991Simulation of Stochastic Processes by Spectral Representation1,379 cited
  • 2008Efficient Global Reliability Analysis for Nonlinear Implicit Performance Functions1,141 cited
  • 2018Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization1,072 cited
  • 2009Stochastic Dynamics of Structures923 cited
  • 1993A new look at the response surface approach for reliability analysis733 cited
  • 2004Application of kriging method to structural reliability problems709 cited
  • 2012A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models692 cited
  • 2023Support vector machine in structural reliability analysis: A review507 cited
  • 2011Reliability-based design optimization using kriging surrogates and subset simulation450 cited
  • 2015MCMC algorithms for Subset Simulation413 cited
  • 2016Assessing small failure probabilities by AK–SS: An active learning method combining Kriging and Subset Simulation

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412 cited
  • 2018An active-learning algorithm that combines sparse polynomial chaos expansions and bootstrap for structural reliability analysis308 cited
  • 2022Active learning for structural reliability: Survey, general framework and benchmark282 cited
  • Cited by

    • 2025Surrogate modeling for probability distribution estimation: Uniform or adaptive design?18 cited
    • 2025Dimensionality reduction can be used as a surrogate model for high-dimensional forward uncertainty quantification13 cited