Modified screening-based Kriging method with cross validation and application to engineering design-Reference-Cited by-同舟云学术

Modified screening-based Kriging method with cross validation and application to engineering design

Published:2019-06 Issue: Volume:70 Page:626-642
ISSN:0307-904X
Container-title:Applied Mathematical Modelling
language:en
Short-container-title:Applied Mathematical Modelling

Author:

Kang Kyeonghwan^ORCID,Qin Caiyan,Lee Bongjae^ORCID,Lee Ikjin

Funder

Korea Institute of Energy Technology Evaluation and PlanningKorea Institute of Energy Technology Evaluation and Planning (KETEP)

Ministry of Trade Industry & EnergyMinistry of Trade Industry & Energy (MOTIE)

National Research Foundation of KoreaNational Research Foundation of Korea (NRF)

Publisher

Elsevier BV

Subject

Applied Mathematics,Modelling and Simulation

Reference42 articles.

1. Adaptive response surface method using inherited latin hypercube design points;Wang;J. Mech. Des.,2003

2. Analysis of support vector regression for approximation of complex engineering analyses;Clarke;J. Mech. Des.,2005

3. An efficient methodology for modeling complex computer codes with Gaussian processes;Marrel;Comput. Stat. Data Anal.,2008

4. Variable selection using Gaussian process regression-based metrics for high-dimensional model approximation with limited data;Lee;Struct. Multidiscip. Optim.,2018

5. On the prediction error variance of three common spatial interpolation schemes;Kyriakidis;Int. J. Geogr. Inf. Sci.,2006

Cited by 35 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A new adaptive Kriging-based optimization (AKBO) framework for constrained optimization problems: A case study on shared autonomous electric vehicle system design;Expert Systems with Applications;2024-10

2. A Kriging-assisted evolutionary algorithm with multiple infill sampling for expensive many-objective optimization;Engineering Applications of Artificial Intelligence;2024-09

3. Statistical model calibration of correlated unknown model variables through identifiability improvement;Probabilistic Engineering Mechanics;2024-07

4. Modified Structure of Deep Neural Network for Training Multi-Fidelity Data With Non-Common Input Variables;Journal of Mechanical Design;2024-03-05

5. A novel sampling method for adaptive gradient-enhanced Kriging;Computer Methods in Applied Mechanics and Engineering;2024-01