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

David E. Bernholdt, Mark R. Ciancosa, David L. Green, Kody J.H. Law, Alexander Litvinenko, Jin M. Park

    Research output: Contribution to journalArticlepeer-review


    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.
    Original languageEnglish (US)
    Pages (from-to)41-53
    Number of pages13
    JournalBig Data and Information Analytics
    Issue number2
    StatePublished - Dec 6 2018

    Bibliographical note

    KAUST Repository Item: Exported on 2020-10-01
    Acknowledgements: 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.


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