Comparing theory based and higher-order reduced models for fusion simulation data
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ArticleAuthors
Bernholdt, David E.Ciancosa, Mark R.
Green, David L.
Law, Kody J.H.
Litvinenko, Alexander

Park, Jin M.
KAUST Department
Extreme Computing Research CenterDate
2018-12-06Online Publication Date
2018-12-06Print Publication Date
2018Permanent link to this record
http://hdl.handle.net/10754/630801
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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.2018.2.41Sponsors
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.Additional Links
http://www.aimspress.com/article/10.3934/BigDIA.2018.2.41ae974a485f413a2113503eed53cd6c53
10.3934/BigDIA.2018.2.41