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    Derivative-based global sensitivity analysis for models with high-dimensional inputs and functional outputs

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    1902.04630.pdf (1.877Mb)
    Links to Files
    https://arxiv.org/pdf/1902.04630.pdf
    Permanent Link
    http://hdl.handle.net/11603/12948
    Collections
    • UMBC Faculty Collection
    • UMBC Mechanical Engineering Department
    Metadata
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    Author/Creator
    Cleaves, Helen L.
    Alexanderian, Alen
    Guy, Hayley
    Smith, Ralph C.
    Yu, Meilin
    Date
    2019-02-17
    Type of Work
    26 pages
    Text
    journal articles preprints
    Citation of Original Publication
    Helen L. Cleaves, Alen Alexanderian, Hayley Guy, Ralph C. Smith and Meilin Yu, Derivative-based global sensitivity analysis for models with high-dimensional inputs and functional outputs, 2019, https://arxiv.org/pdf/1902.04630.pdf
    Rights
    This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
    Subjects
    global sensitivity analysis
    derivative-based global sensitivity measures (DGSMs)
    functional Sobol' indices
    Karhunen-Loeve expansions
    Abstract
    We present a framework for derivative-based global sensitivity analysis (GSA) for models with high-dimensional input parameters and functional outputs. We combine ideas from derivative-based GSA, random eld representation via Karhunen-Loeve expansions, and adjoint- based gradient computation to provide a scalable computational framework for computing the pro- posed derivative-based GSA measures. We illustrate the strategy for a nonlinear ODE model of cholera epidemics and for elliptic PDEs with application examples from geosciences and biotrans- port


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    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3544


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.