A Bayes network approach to uncertainty quantification in hierarchically developed computational models

Urbina, Angel; Mahadevan, Sankaran; Paez, Thomas L.

doi:10.1615/Int.J.UncertaintyQuantification.v2.i2.70

Title: A Bayes network approach to uncertainty quantification in hierarchically developed computational models

Abstract

Here, performance assessment of complex systems is ideally accomplished through system-level testing, but because they are expensive, such tests are seldom performed. On the other hand, for economic reasons, data from tests on individual components that are parts of complex systems are more readily available. The lack of system-level data leads to a need to build computational models of systems and use them for performance prediction in lieu of experiments. Because their complexity, models are sometimes built in a hierarchical manner, starting with simple components, progressing to collections of components, and finally, to the full system. Quantification of uncertainty in the predicted response of a system model is required in order to establish confidence in the representation of actual system behavior. This paper proposes a framework for the complex, but very practical problem of quantification of uncertainty in system-level model predictions. It is based on Bayes networks and uses the available data at multiple levels of complexity (i.e., components, subsystem, etc.). Because epistemic sources of uncertainty were shown to be secondary, in this application, aleatoric only uncertainty is included in the present uncertainty quantification. An example showing application of the techniques to uncertainty quantification of measures of response of amore »« less

Authors:

Urbina, Angel ^[1]; Mahadevan, Sankaran ^[2]; Paez, Thomas L. ^[3]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Vanderbilt Univ., Nashville, TN (United States)
Thomas-Paez Consulting, Durango, CO (United States)

Publication Date:: Thu Mar 01 00:00:00 EST 2012

Research Org.:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:: 1344496

Report Number(s):: SAND-2011-4666J
Journal ID: ISSN 2152-5080; 596984

Grant/Contract Number:: AC04-94AL85000

Resource Type:: Accepted Manuscript

Journal Name:: International Journal for Uncertainty Quantification

Additional Journal Information:: Journal Volume: 2; Journal Issue: 2; Journal ID: ISSN 2152-5080

Publisher:: Begell House

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; uncertainty quantification; Markov chain Monte Carlo; Bayesian inference; hierarchical model development; structural dynamics

Citation Formats


                    Urbina, Angel, Mahadevan, Sankaran, and Paez, Thomas L. A Bayes network approach to uncertainty quantification in hierarchically developed computational models.  United States: N. p., 2012. 
Web.  doi:10.1615/Int.J.UncertaintyQuantification.v2.i2.70.

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                    Urbina, Angel, Mahadevan, Sankaran, & Paez, Thomas L. A Bayes network approach to uncertainty quantification in hierarchically developed computational models.  United States.  https://doi.org/10.1615/Int.J.UncertaintyQuantification.v2.i2.70

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                    Urbina, Angel, Mahadevan, Sankaran, and Paez, Thomas L. Thu .  
"A Bayes network approach to uncertainty quantification in hierarchically developed computational models".  United States.  https://doi.org/10.1615/Int.J.UncertaintyQuantification.v2.i2.70.  https://www.osti.gov/servlets/purl/1344496.

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@article{osti_1344496,

  title        = {A Bayes network approach to uncertainty quantification in hierarchically developed computational models},

  author       = {Urbina, Angel and Mahadevan, Sankaran and Paez, Thomas L.},

  abstractNote = {Here, performance assessment of complex systems is ideally accomplished through system-level testing, but because they are expensive, such tests are seldom performed. On the other hand, for economic reasons, data from tests on individual components that are parts of complex systems are more readily available. The lack of system-level data leads to a need to build computational models of systems and use them for performance prediction in lieu of experiments. Because their complexity, models are sometimes built in a hierarchical manner, starting with simple components, progressing to collections of components, and finally, to the full system. Quantification of uncertainty in the predicted response of a system model is required in order to establish confidence in the representation of actual system behavior. This paper proposes a framework for the complex, but very practical problem of quantification of uncertainty in system-level model predictions. It is based on Bayes networks and uses the available data at multiple levels of complexity (i.e., components, subsystem, etc.). Because epistemic sources of uncertainty were shown to be secondary, in this application, aleatoric only uncertainty is included in the present uncertainty quantification. An example showing application of the techniques to uncertainty quantification of measures of response of a real, complex aerospace system is included.},

  doi          = {10.1615/Int.J.UncertaintyQuantification.v2.i2.70},

  journal      = {International Journal for Uncertainty Quantification},

  number       = 2,

  volume       = 2,

  place        = {United States},

  year         = {Thu Mar 01 00:00:00 EST 2012},

  month        = {Thu Mar 01 00:00:00 EST 2012}

}

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