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Role of calibration, validation, and relevance in multi-level uncertainty integration

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  • Li, Chenzhao
  • Mahadevan, Sankaran

Abstract

Calibration of model parameters is an essential step in predicting the response of a complicated system, but the lack of data at the system level makes it impossible to conduct this quantification directly. In such a situation, system model parameters are estimated using tests at lower levels of complexity which share the same model parameters with the system. For such a multi-level problem, this paper proposes a methodology to quantify the uncertainty in the system level prediction by integrating calibration, validation and sensitivity analysis at different levels. The proposed approach considers the validity of the models used for parameter estimation at lower levels, as well as the relevance at the lower level to the prediction at the system level. The model validity is evaluated using a model reliability metric, and models with multivariate output are considered. The relevance is quantified by comparing Sobol indices at the lower level and system level, thus measuring the extent to which a lower level test represents the characteristics of the system so that the calibration results can be reliably used in the system level. Finally the results of calibration, validation and relevance analysis are integrated in a roll-up method to predict the system output.

Suggested Citation

  • Li, Chenzhao & Mahadevan, Sankaran, 2016. "Role of calibration, validation, and relevance in multi-level uncertainty integration," Reliability Engineering and System Safety, Elsevier, vol. 148(C), pages 32-43.
    • Handle: RePEc:eee:reensy:v:148:y:2016:i:c:p:32-43
    DOI: 10.1016/j.ress.2015.11.013
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    References listed on IDEAS

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    1. Scott Ferson & William L. Oberkampf, 2009. "Validation of imprecise probability models," International Journal of Reliability and Safety, Inderscience Enterprises Ltd, vol. 3(1/2/3), pages 3-22.
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    3. Rebba, Ramesh & Mahadevan, Sankaran, 2008. "Computational methods for model reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 93(8), pages 1197-1207.
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    Cited by:

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    7. Yoo, Yeongmin & Jung, Ui-Jin & Han, Yong Ha & Lee, Jongsoo, 2021. "Data Augmentation-Based Prediction of System Level Performance under Model and Parameter Uncertainties: Role of Designable Generative Adversarial Networks (DGAN)," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
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