An efficient method combining active learning kriging and Monte Carlo simulation for profust failure probability
DOI10.1016/j.fss.2019.02.003zbMath1452.62732OpenAlexW2913046542MaRDI QIDQ2219160
Chunyan Ling, Minjie Wang, Bo Sun, Zhen-Zhou Lü
Publication date: 19 January 2021
Published in: Fuzzy Sets and Systems (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.fss.2019.02.003
Monte Carlo simulationGaussian quadraturefuzzy stateactive learning krigingprofust failure probabilityU-learning function
Monte Carlo methods (65C05) Learning and adaptive systems in artificial intelligence (68T05) Reliability and life testing (62N05) Fuzziness, and survival analysis and censored data (62N86)
Related Items (2)
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Cites Work
- Profust reliability of a gracefully degradable system
- A solution algorithm for fuzzy linear programming with piecewise linear membership functions
- Fuzzy variables as a basis for a theory of fuzzy reliability in the possibility context
- Efficient global optimization of expensive black-box functions
- Fuzzy sets and membership functions
- Design and analysis of computer experiments. With comments and a rejoinder by the authors
- Global sensitivity analysis using support vector regression
- Borgonovo moment independent global sensitivity analysis by Gaussian radial basis function meta-model
- A unified method and its application to brake instability analysis involving different types of epistemic uncertainties
- Bayesian sparse polynomial chaos expansion for global sensitivity analysis
- Time-dependent failure possibility analysis under consideration of fuzzy uncertainty
- Efficient structural reliability analysis method based on advanced kriging model
- Monte Carlo strategies in scientific computing
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