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    Comparing theory based and higher-order reduced models for fusion simulation data

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    BigDIA-03-02-041.pdf
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    Description:
    Published Research Article
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    Type
    Article
    Authors
    Bernholdt, David E.
    Ciancosa, Mark R.
    Green, David L.
    Law, Kody J.H.
    Litvinenko, Alexander cc
    Park, Jin M.
    KAUST Department
    Extreme Computing Research Center
    Date
    2018-12-06
    Online Publication Date
    2018-12-06
    Print Publication Date
    2018
    Permanent link to this record
    http://hdl.handle.net/10754/630801
    
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    Abstract
    We consider using regression to fit a theory-based log-linear ansatz, as well as higher order approximations, for the thermal energy confinement of a Tokamak as a function of device features. We use general linear models based on total order polynomials, as well as deep neural networks. The results indicate that the theory-based model fits the data almost as well as the more sophisticated machines, within the support of the data set. The conclusion we arrive at is that only negligible improvements can be made to the theoretical model, for input data of this type.
    Citation
    David E. Bernholdt, Mark R. Ciancosa, David L. Green, Kody J.H. Law, Alexander Litvinenko, Jin M. Park. Comparing theory based and higher-order reduced models for fusion simulation data. Big Data and Information Analytics, 2018, 3(2): 41-53. doi: 10.3934/BigDIA.2018.2.41
    Sponsors
    This work has been supported by the U.S. Department of Energy, Office of Science, Office of Fusion Energy Sciences, using the DIII-D National Fusion Facility, a DOE Office of Science user facility under awards, DE-FG02-04ER54761 and DE-FC02-04ER54698.
    Publisher
    American Institute of Mathematical Sciences (AIMS)
    Journal
    Big Data and Information Analytics
    DOI
    10.3934/BigDIA.2018.2.41
    Additional Links
    http://www.aimspress.com/article/10.3934/BigDIA.2018.2.41
    ae974a485f413a2113503eed53cd6c53
    10.3934/BigDIA.2018.2.41
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