A new learning function for Kriging and its applications to solve reliability problems in engineering

By Zhaoyang Lv, Zhenzhou Lu, Pan Wang. Computers and Mathematics with Applications, 2015

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Active learning for structural reliability: Survey, general framework and benchmark (2021, background): “, 2020), radial basis importance sampling (Bo and HuiFeng, 2018) or line sampling (Lv et al., 2015).”
Rare event estimation with sequential directional importance sampling (2022, methodology): “Numerical simulation methods include Monte Carlo simulation (MCS) and variance reduction methods such as importance sampling (IS) [18-20] and its enhanced versions [21-24], subset simulation (SuS) [15, 16, 25], thermodynamic integration and parallel tempering (TIPT) [26], line sampling (LS) [27-29],”
LIF: A new Kriging based learning function and its application to structural reliability analysis (2017, background): “Various kinds of sequential DoE based on the Kriging model have been proposed to improve the accuracy of structural reliability analysis and reduce the number of calls to the real performance function [8,29,32,38,41,42].”