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Activity Number:
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532
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 2, 2007 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #309207 |
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Title:
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Aleatory and Epistemic Uncertainty Quantification for Engineering Applications
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Author(s):
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Laura Swiler*+ and Anthony Giunta
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Companies:
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Sandia National Laboratories and Sandia National Laboratories
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Address:
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Sandia Labs PO Box 5800, Albuquerque, NM, 87185,
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Keywords:
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uncertainty quantification ; engineering models ; aleatory ; epistemic
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Abstract:
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Most computer models for engineering applications are developed to help assess a design or regulatory requirement. As part of this task, the capability to quantify the impact of variability and uncertainty in the decision context is critical. The requirement is often stated as: the probability that some system response quantity exceeds a threshold value is less than some required probability. This presentation will provide an outline and comparison of methods that are used for analyzing and propagating aleatory and epistemic uncertainties. The methods are all available in a software tool called DAKOTA. We will specifically focus on four classes of methods: Latin Hypercube sampling, Dempster-Shafer theory of evidence, "second-order" probability analysis, and analytic reliability methods. Examples of each of the methods as applied to a simple engineering model will be provided.
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