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Variable-fidelity model selection for stochastic simulation

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  • Mullins, Joshua
  • Mahadevan, Sankaran

Abstract

This paper presents a model selection methodology for maximizing the accuracy in the predicted distribution of a stochastic output of interest subject to an available computational budget. Model choices of different resolutions/fidelities such as coarse vs. fine mesh and linear vs. nonlinear material model are considered. The proposed approach makes use of efficient simulation techniques and mathematical surrogate models to develop a model selection framework. The model decision is made by considering the expected (or estimated) discrepancy between model prediction and the best available information about the quantity of interest, as well as the simulation effort required for the particular model choice. The form of the best available information may be the result of a maximum fidelity simulation, a physical experiment, or expert opinion. Several different situations corresponding to the type and amount of data are considered for a Monte Carlo simulation over the input space. The proposed methods are illustrated for a crack growth simulation problem in which model choices must be made for each cycle or cycle block even within one input sample.

Suggested Citation

  • Mullins, Joshua & Mahadevan, Sankaran, 2014. "Variable-fidelity model selection for stochastic simulation," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 40-52.
    • Handle: RePEc:eee:reensy:v:131:y:2014:i:c:p:40-52
    DOI: 10.1016/j.ress.2014.06.011
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    References listed on IDEAS

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    1. Bichon, Barron J. & McFarland, John M. & Mahadevan, Sankaran, 2011. "Efficient surrogate models for reliability analysis of systems with multiple failure modes," Reliability Engineering and System Safety, Elsevier, vol. 96(10), pages 1386-1395.
    2. 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.
    3. Peter D. Grünwald, 2007. "The Minimum Description Length Principle," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262072815, December.
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    Cited by:

    1. Meng, Huixing & Kloul, Leïla & Rauzy, Antoine, 2018. "Modeling patterns for reliability assessment of safety instrumented systems," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 111-123.

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