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Big Data and Information Analytics, 2018, 3(2): 41-53. doi: 10.3934/BigDIA.2018.2.41. Research article

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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

1 Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
2 Fusion Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
3 School of Mathematics, University of Manchester, UK
4 Extreme Computing Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
5 General Atomics, San Diego, CA, USA

Received: , Accepted: , Published:

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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.

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Keywords: general linear models; polynomial models; deep neural networks; plasma fusion; Tokamak

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

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