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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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    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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