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

Journal Article · 05 December 2018 · Big Data and Information Analytics (Online)

DOI: https://doi.org/10.3934/BigDIA.2018.2.41 · OSTI ID:1486929

E. Bernholdt, David ^[1]; R. Ciancosa, Mark ^[1]; L. Green, David ^[1]; J.H. Law, Kody ^[2]; Litvinenko, Alexander ^[3]; M. Park, Jin ^[4]

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1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
2. Univ. of Manchester (United Kingdom)
3. King Abdullah Univ. of Science and Technology, Thuwal (Saudi Arabia)
4. General Atomics, San Diego, CA (United States)

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.

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1486929
• Journal Information: Big Data and Information Analytics (Online), Journal Name: Big Data and Information Analytics (Online) Journal Issue: 2 Vol. 3; ISSN 2380-6974
• Publisher: AIMS PressCopyright Statement
• Country of Publication: United States
• Language: English