Enhanced Kriging leave-one-out cross-validation in improving model estimation and optimization-Reference-Cited by-同舟云学术

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Enhanced Kriging leave-one-out cross-validation in improving model estimation and optimization

Published:2023-09 Issue: Volume:414 Page:116194
ISSN:0045-7825
Container-title:Computer Methods in Applied Mechanics and Engineering
language:en
Short-container-title:Computer Methods in Applied Mechanics and Engineering

Author:

Pang Yong,Wang Yitang,Lai Xiaonan,Zhang Shuai,Liang Pengwei,Song Xueguan

Funder

National Natural Science Foundation of China

Ministry of Science and Technology of the People's Republic of China

National Key Research and Development Program of China

Publisher

Elsevier BV

Subject

Computer Science Applications,General Physics and Astronomy,Mechanical Engineering,Mechanics of Materials,Computational Mechanics

Reference58 articles.

1. OTL-PEM: An optimization-based two-layer pointwise ensemble of surrogate models;Pang;J. Mech. Des.,2022

2. Efficient local adaptive Kriging approximation method with single-loop strategy for reliability-based design optimization;Yang;Comput. Methods Appl. Mech. Engrg.,2022

3. An expensive many-objective optimization algorithm based on efficient expected hypervolume improvement;Pang;IEEE Trans. Evol. Comput.,2022

4. Constraint boundary pursuing - based surrogate - assisted differential evolution for expensive optimization problems with mixed constraints;Yang;Struct. Multidiscip. Optim.,2023

5. A novel nested stochastic Kriging model for response noise quantification and reliability analysis;Hao;Comput. Methods Appl. Mech. Engrg.,2021

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