Development of a non-parametric Gaussian process model in the three-dimensional equilibrium reconstruction code V3FIT

Howell, Eric C.; Hanson, J. D.

doi:10.1017/s0022377819000813

Title: Development of a non-parametric Gaussian process model in the three-dimensional equilibrium reconstruction code V3FIT

Journal Article · 13 January 2020 · Journal of Plasma Physics

DOI: https://doi.org/10.1017/s0022377819000813 · OSTI ID:1800135

^[1]; Hanson, J. D. ^[2]

Tech-X Corporation, Boulder, CO (United States); OSTI
Auburn Univ., AL (United States). Dept. of Physics

A non-parametric Gaussian process regression model is developed in the three-dimensional equilibrium reconstruction code V3FIT. A Gaussian process is a normal distribution of functions that is uniquely defined by specifying a mean function and covariance kernel function. Gaussian process regression assumes that an unknown profile belongs to a particular Gaussian process and uses Bayesian analysis to select the function the give the best fit to measured data. The implementation in V3FIT uses a hybrid representation where Gaussian processes are used to infer some of the equilibrium profiles and standard parametric techniques are used to infer the remaining profiles. The implementation of the Gaussian process is tested using both synthetic data and experimental data from multiple machines.

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Research Organization:: Auburn Univ., AL (United States); Tech-X Corporation, Boulder, CO (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Fusion Energy Sciences (FES)

Grant/Contract Number:: FG02-03ER54692; SC0018313

OSTI ID:: 1800135

Journal Information:: Journal of Plasma Physics, Journal Name: Journal of Plasma Physics Journal Issue: 1 Vol. 86; ISSN 0022-3778

Publisher:: Cambridge University PressCopyright Statement

Country of Publication:: United States

Language:: English

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