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Designing combined physical and computer experiments to maximize prediction accuracy

Author

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  • Leatherman, Erin R.
  • Dean, Angela M.
  • Santner, Thomas J.

Abstract

Combined designs for experiments involving a physical system and a simulator of the physical system are evaluated in terms of their accuracy of predicting the mean of the physical system. Comparisons are made among designs that are (1) locally optimal under the minimum integrated mean squared prediction error criterion for the combined physical system and simulator experiments, (2) locally optimal for the physical or simulator experiments, with a fixed design for the component not being optimized, (3) maximin augmented nested Latin hypercube, and (4) I-optimal for the physical system experiment and maximin Latin hypercube for the simulator experiment. Computational methods are proposed for constructing the designs of interest. For a large test bed of examples, the empirical mean squared prediction errors are compared at a grid of inputs for each test surface using a statistically calibrated Bayesian predictor based on the data from each design. The prediction errors are also studied for a test bed that varies only the calibration parameter of the test surface. Design recommendations are given.

Suggested Citation

  • Leatherman, Erin R. & Dean, Angela M. & Santner, Thomas J., 2017. "Designing combined physical and computer experiments to maximize prediction accuracy," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 346-362.
    • Handle: RePEc:eee:csdana:v:113:y:2017:i:c:p:346-362
    DOI: 10.1016/j.csda.2016.07.013
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    References listed on IDEAS

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    1. Williams, Brian J. & Loeppky, Jason L. & Moore, Leslie M. & Macklem, Mason S., 2011. "Batch sequential design to achieve predictive maturity with calibrated computer models," Reliability Engineering and System Safety, Elsevier, vol. 96(9), pages 1208-1219.
    2. 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.
    3. 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.
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    Cited by:

    1. 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.

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