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

doi:10.3934/BigDIA.2018.2.41

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 journal › Article › peer-review

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.

Original language	English (US)
Pages (from-to)	41-53
Number of pages	13
Journal	Big Data and Information Analytics
Volume	3
Issue number	2
DOIs	https://doi.org/10.3934/BigDIA.2018.2.41
State	Published - 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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10.3934/BigDIA.2018.2.41

https://repository.kaust.edu.sa/bitstream/10754/630801/1/BigDIA-03-02-041.pdf

Cite this

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title = "Comparing theory based and higher-order reduced models for fusion simulation data",

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.",

author = "Bernholdt, {David E.} and Ciancosa, {Mark R.} and Green, {David L.} and Law, {Kody J.H.} and Alexander Litvinenko and Park, {Jin M.}",

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.",

year = "2018",

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doi = "10.3934/BigDIA.2018.2.41",

language = "English (US)",

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T1 - Comparing theory based and higher-order reduced models for fusion simulation data

AU - Bernholdt, David E.

AU - Ciancosa, Mark R.

AU - Green, David L.

AU - Law, Kody J.H.

AU - Litvinenko, Alexander

AU - Park, Jin M.

N1 - 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.

PY - 2018/12/6

Y1 - 2018/12/6

N2 - 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.

AB - 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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DO - 10.3934/BigDIA.2018.2.41

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SP - 41

EP - 53

JO - Big Data and Information Analytics

JF - Big Data and Information Analytics

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ER -

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

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