Numerical investigation for erratic behavior of Kriging surrogate model

被引:17
|
作者
Kwon, Hyungil [1 ]
Yi, Seulgi [1 ]
Choi, Seongim [2 ]
机构
[1] Korea Adv Inst Sci & Technol, Div Aerosp Engn, Taejon 305701, South Korea
[2] Virginia Polytech Inst & State Univ, Dept Aerosp & Ocean Engn, Blacksburg, VA 24061 USA
基金
新加坡国家研究基金会;
关键词
Kriging model; Likelihood function; Correlation matrix; Surrogate model; Design optimization; PENALIZED LIKELIHOOD; COMPUTER EXPERIMENTS;
D O I
10.1007/s12206-014-0831-x
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Kriging model is one of popular spatial/temporal interpolation models in engineering field since it could reduce the time resources for the expensive analysis. But generation of the Kriging model is hardly a sinecure because internal semi-variogram structure of the Kriging often reveals numerically unstable or erratic behaviors. In present study, the issues in the maximum likelihood estimation which are the vital-parts of the construction of the Kriging model, is investigated. These issues are divided into two aspects; Issue I is for the erratic response of likelihood function itself, and Issue II is for numerically unstable behaviors in the correlation matrix. For both issues, studies for specific circumstances which might raise the issue, and the reason of that are conducted. Some practical ways further are suggested to cope with them. Furthermore, the issue is studied for practical problem; aerodynamic performance coefficients of two-dimensional airfoil predicted by CFD analysis. Result shows that such erratic behavior of Kriging surrogate model can be effectively resolved by proposed solution. In conclusion, it is expected this paper could be helpful to prevent such an erratic and unstable behavior.
引用
收藏
页码:3697 / 3707
页数:11
相关论文
共 50 条
  • [11] Research on geoacoustic inversion based on Kriging surrogate model
    Zhang, Lilun
    Guo, Xianpeng
    Lu, Zengquan
    Wang, Dezhi
    Wang, Yongxian
    2017 IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, COMMUNICATIONS AND COMPUTING (ICSPCC), 2017,
  • [12] An Adaptive Searching Kriging Surrogate Model for Aerodynamic Optimization
    Liang Xu L
    Tang, Zhili
    Feng, Wenliang
    4TH INTERNATIONAL CONFERENCE ON FLUID MECHANICS AND INDUSTRIAL APPLICATIONS (FMIA 2020), 2020, 1600
  • [13] Experimental and numerical investigation of blast wave impact on a surrogate head model
    Banton, R.
    Piehler, T.
    Zander, N.
    Benjamin, R.
    Mrozek, R.
    Duckworth, J.
    Petel, O.
    SHOCK WAVES, 2021, 31 (05) : 481 - 498
  • [14] Experimental and numerical investigation of blast wave impact on a surrogate head model
    R. Banton
    T. Piehler
    N. Zander
    R. Benjamin
    R. Mrozek
    J. Duckworth
    O. Petel
    Shock Waves, 2021, 31 : 481 - 498
  • [15] Characterization of the dynamic behavior of structures using the Kriging surrogate and experimental data
    Gubaua, Jose Eduardo
    Dicati, Gabriela Wessling Oening
    da Silva, Thiago
    Lopes, Eduardo Marcio de Oliveira
    Pereira, Jucelio Tomas
    Bavastri, Carlos Alberto
    ACTA MECHANICA, 2023, 234 (10) : 4611 - 4627
  • [16] A Kriging Surrogate Model for Computing Gas Mixture Equations of State
    Ouellet, Frederick
    Park, Chanyoung
    Rollin, Bertrand
    Haftka, Raphael T.
    Balachandar, S.
    JOURNAL OF FLUIDS ENGINEERING-TRANSACTIONS OF THE ASME, 2019, 141 (09):
  • [17] An adaptive sampling method for Kriging surrogate model with multiple outputs
    Zhai, Zhangming
    Li, Haiyang
    Wang, Xugang
    ENGINEERING WITH COMPUTERS, 2022, 38 (SUPPL 1) : 277 - 295
  • [18] Operation optimization of hydrocracking process based on Kriging surrogate model
    Zhong, Weimin
    Qiao, Cheng
    Peng, Xin
    Li, Zhi
    Fan, Chen
    Qian, Feng
    CONTROL ENGINEERING PRACTICE, 2019, 85 : 34 - 40
  • [19] An Improved Blind Kriging Surrogate Model for Design Optimization Problems
    Mai, Hau T.
    Lee, Jaewook
    Kang, Joowon
    Nguyen-Xuan, H.
    Lee, Jaehong
    MATHEMATICS, 2022, 10 (16)
  • [20] An adaptive sampling method for Kriging surrogate model with multiple outputs
    Zhangming Zhai
    Haiyang Li
    Xugang Wang
    Engineering with Computers, 2022, 38 : 277 - 295