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Model Validation in Scientific Computing: Considering Robustness to Non-probabilistic Uncertainty in the Input Parameters

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Model Validation and Uncertainty Quantification, Volume 3

Abstract

The origin of the term validation traces to the Latin valere, meaning worth. In the context of scientific computing, validation aims to determine the worthiness of a model in regard to its support of critical decision making. This determination of worthiness must occur in the face of unavoidable idealizations in the mathematical representation of the phenomena the model is intended to represent. These models are often parameterized further complicating the validation problem due to the need to determine appropriate parameter values for the imperfect mathematical representations. The determination of worthiness then becomes assessing whether an unavoidably imperfect mathematical model, subjected to poorly known input parameters, can predict sufficiently well to serve its intended purpose. To achieve this, we herein evaluate the agreement between a model’s predictions and associated experiments as well as the robustness of this agreement given imperfections in both the model’s mathematical representation of reality as well as its input parameter values.

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Notes

  1. 1.

    Note that we have also conveniently assumed that model yields converged solutions within the time and spatial domains and that numerical uncertainties are of little importance.

  2. 2.

    Of course, the observables must be in sufficient quality and quantity to identify the model’s flaws.

  3. 3.

    For a model with two parameters, the size of a satisfying boundary would be defined by its area, while for a model with multiple parameters, the size would be defined by a hypervolume.

References

  1. Weinmann A (1991) Uncertain models and robust control. Springer-Verlag, Vienna

    Book  Google Scholar 

  2. Elishakoff I (1995) Essay on uncertainties in elastic and viscoelastic structures: from A. M. Freudenthal’s criticisms to modern convex modeling. Comput Struct 56(6):871–895

    Article  MATH  Google Scholar 

  3. Ben-Haim Y (2001) Information-gap decision theory: decisions under severe uncertainty. Academic Press, San Diego, California, USA

    Google Scholar 

  4. Atamturktur S, Liu Z, Cogan S, Juang CH (2014) Calibration of imprecise and inaccurate numerical models considering fidelity and robustness: a multi-objective optimization based approach. Struct Multidiscip Optim. doi:10.1007/s00158-014-1159-y

  5. Sakurai S, Ellingwood B, Kushiyama S (2001) Probabilistic study of the behavior of steel frames with partially restrained connections. Eng Struct 23(11):1410–1417

    Article  Google Scholar 

  6. Díaz C, Victoria M, Querin OM, Martí P (2012) Optimum design of semi-rigid connections using metamodels. J Constr Steel Res 78:97–106

    Article  Google Scholar 

  7. Hartmann F, Katz C (2004) Structural analysis with finite elements. Springer-Verlag, Berlin

    Book  MATH  Google Scholar 

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Correspondence to Sez Atamturktur .

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© 2015 The Society for Experimental Mechanics, Inc.

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Roche, G., Prabhu, S., Shields, P., Atamturktur, S. (2015). Model Validation in Scientific Computing: Considering Robustness to Non-probabilistic Uncertainty in the Input Parameters. In: Atamturktur, H., Moaveni, B., Papadimitriou, C., Schoenherr, T. (eds) Model Validation and Uncertainty Quantification, Volume 3. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-15224-0_20

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  • DOI: https://doi.org/10.1007/978-3-319-15224-0_20

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-15223-3

  • Online ISBN: 978-3-319-15224-0

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