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Validation and error estimation of computational models

Author

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  • Rebba, Ramesh
  • Mahadevan, Sankaran
  • Huang, Shuping

Abstract

This paper develops a Bayesian methodology for assessing the confidence in model prediction by comparing the model output with experimental data when both are stochastic. The prior distribution of the response is first computed, which is then updated based on experimental observation using Bayesian analysis to compute a validation metric. A model error estimation methodology is then developed to include model form error, discretization error, stochastic analysis error (UQ error), input data error and output measurement error. Sensitivity of the validation metric to various error components and model parameters is discussed. A numerical example is presented to illustrate the proposed methodology.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:91:y:2006:i:10:p:1390-1397
    DOI: 10.1016/j.ress.2005.11.035
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    Citations

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    Cited by:

    1. Ling, You & Mahadevan, Sankaran, 2013. "Quantitative model validation techniques: New insights," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 217-231.
    2. Sankararaman, Shankar & Mahadevan, Sankaran, 2011. "Model validation under epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 96(9), pages 1232-1241.
    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. Ray, Douglas & Ramirez-Marquez, Jose, 2020. "A framework for probabilistic model-based engineering and data synthesis," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    5. An, Dawn & Kim, Nam H. & Choi, Joo-Ho, 2015. "Practical options for selecting data-driven or physics-based prognostics algorithms with reviews," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 223-236.
    6. Ao, Dan & Hu, Zhen & Mahadevan, Sankaran, 2017. "Design of validation experiments for life prediction models," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 22-33.
    7. Mullins, Joshua & Ling, You & Mahadevan, Sankaran & Sun, Lin & Strachan, Alejandro, 2016. "Separation of aleatory and epistemic uncertainty in probabilistic model validation," Reliability Engineering and System Safety, Elsevier, vol. 147(C), pages 49-59.
    8. Maupin, Kathryn A. & Swiler, Laura P., 2020. "Model discrepancy calibration across experimental settings," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    9. Bjerga, Torbjørn & Aven, Terje & Zio, Enrico, 2014. "An illustration of the use of an approach for treating model uncertainties in risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 125(C), pages 46-53.
    10. Trivedi, Chirag & Cervantes, Michel J., 2017. "Fluid-structure interactions in Francis turbines: A perspective review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 87-101.

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