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Model discrepancy calibration across experimental settings

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  • Maupin, Kathryn A.
  • Swiler, Laura P.

Abstract

Despite continuing advances in the reliability of computational modeling and simulation, model inadequacy remains a pervasive concern across scientific disciplines. Further challenges are introduced into the already complex problem of “correcting†an inadequate model when experimental data is collected at varying experimental settings. This paper introduces a general approach to calibrating a model discrepancy function when the model is expected to perform for multiple experimental configurations and give predictions as a function of temporal and/or spatial coordinates.

Suggested Citation

  • Maupin, Kathryn A. & Swiler, Laura P., 2020. "Model discrepancy calibration across experimental settings," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    • Handle: RePEc:eee:reensy:v:200:y:2020:i:c:s0951832019301802
    DOI: 10.1016/j.ress.2020.106818
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    References listed on IDEAS

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    1. Park, Inseok & Grandhi, Ramana V., 2014. "A Bayesian statistical method for quantifying model form uncertainty and two model combination methods," Reliability Engineering and System Safety, Elsevier, vol. 129(C), pages 46-56.
    2. Rebba, Ramesh & Mahadevan, Sankaran & Huang, Shuping, 2006. "Validation and error estimation of computational models," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1390-1397.
    3. Sankararaman, Shankar & Mahadevan, Sankaran, 2015. "Integration of model verification, validation, and calibration for uncertainty quantification in engineering systems," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 194-209.
    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. Matthew Plumlee, 2017. "Bayesian Calibration of Inexact Computer Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1274-1285, July.
    6. 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.
    7. J. L. Brown & L. B. Hund, 2018. "Estimating material properties under extreme conditions by using Bayesian model calibration with functional outputs," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(4), pages 1023-1045, August.
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    Cited by:

    1. Jung, Yongsu & Jo, Hwisang & Choo, Jeonghwan & Lee, Ikjin, 2022. "Statistical model calibration and design optimization under aleatory and epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 222(C).

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