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A D-Optimal Sequential Calibration Design for Computer Models

Author

Listed:
  • Huaimin Diao

    (School of Mathematics and Statistics, Beijing Institute of Technology, Beijing 100081, China)

  • Yan Wang

    (School of Statistics and Data Science, Beijing University of Technology, Beijing 100124, China)

  • Dianpeng Wang

    (School of Mathematics and Statistics, Beijing Institute of Technology, Beijing 100081, China)

Abstract

The problem with computer model calibration by tuning the parameters associated with computer models is significant in many engineering and scientific applications. Although several methods have been established to estimate the calibration parameters, research focusing on the design of calibration parameters remains limited. Therefore, this paper proposes a sequential computer experiment design based on the D-optimal criterion, which can efficiently tune the calibration parameters while improving the prediction ability of the calibrated computer model. Numerical comparisons of the simulated and real data demonstrate the efficiency of the proposed technique.

Suggested Citation

  • Huaimin Diao & Yan Wang & Dianpeng Wang, 2022. "A D-Optimal Sequential Calibration Design for Computer Models," Mathematics, MDPI, vol. 10(9), pages 1-15, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1375-:d:797772
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    References listed on IDEAS

    as
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    4. Higdon, Dave & Gattiker, James & Williams, Brian & Rightley, Maria, 2008. "Computer Model Calibration Using High-Dimensional Output," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 570-583, June.
    5. Marc C. Kennedy & Anthony O'Hagan, 2001. "Bayesian calibration of computer models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 425-464.
    6. Ray-Bing Chen & Yuan Wang & C. F. Jeff Wu, 2020. "Finding optimal points for expensive functions using adaptive RBF-based surrogate model via uncertainty quantification," Journal of Global Optimization, Springer, vol. 77(4), pages 919-948, August.
    7. Antony M. Overstall & David C. Woods, 2016. "Multivariate emulation of computer simulators: model selection and diagnostics with application to a humanitarian relief model," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(4), pages 483-505, August.
    8. Cox, Dennis D. & Park, Jeong-Soo & Singer, Clifford E., 2001. "A statistical method for tuning a computer code to a data base," Computational Statistics & Data Analysis, Elsevier, vol. 37(1), pages 77-92, July.
    Full references (including those not matched with items on IDEAS)

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