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Author: Eric C Howell
Requested Type: Poster
Submitted: 2017-03-17 11:55:48

Co-authors: J. D. Hanson, X. Ma

Contact Info:
Auburn University
206 Allison Lab, Auburn Univer
Auburn, Alabama   36849
United States

Abstract Text:
A non-parametric Gaussian process regression model is developed in the 3-D 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 experimental data from the Compact Toroidal Hybrid experiment (CTH). These reconstructions use Gaussian processes to infer the emissivity profiles for a two-color soft X-ray diagnostic. Standard parametric models are used to represent the density, pressure, and current profiles. A quasi-Newton iteration is used to simultaneously converge on the optimal set of model parameters and kernel hyper-parameters. The hybrid reconstructions are compared with fully parametric reconstructions.

This work was supported by Auburn University and the U.S. DOE through Award Numbers DE-FG02-03ER54692

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