Novel learning functions design based on the probability of improvement criterion and normalization techniques

DOI10.1016/j.apm.2022.03.029zbMath1503.62075OpenAlexW4226329296MaRDI QIDQ2109615

Zequan Chen, Guofa Li, Jialong He, Zhaojun Yang

Publication date: 21 December 2022

Published in: Applied Mathematical Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.apm.2022.03.029

zbMATH Keywords

surrogate modelstructural reliabilitylearning functionadaptive truncated sampling area

Mathematics Subject Classification ID

Optimization of other properties in solid mechanics (74P10) Neural nets and related approaches to inference from stochastic processes (62M45)

Uses Software

• COMSOL
• AK-MCS

Cites Work

• A new learning function for kriging and its applications to solve reliability problems in engineering
• Sequential Bayesian optimal experimental design for structural reliability analysis
• M5 model tree and Monte Carlo simulation for efficient structural reliability analysis
• A hybrid self-adaptive conjugate first order reliability method for robust structural reliability analysis
• Enhanced sequential approximate programming using second order reliability method for accurate and efficient structural reliability-based design optimization
• An adaptive multiple-kriging-surrogate method for time-dependent reliability analysis

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