Data augmentation for disruption prediction via robust surrogate models
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Author(s)
Rath, Katharina
Rügamer, David
Bischl, Bernd
von Toussaint, Udo
Rea, Cristina
Maris, Andrew D.
Granetz, Robert
Albert, Christopher G.
Date Issued
May 2022
Journal
Journal of Plasma Physics
Publisher
Cambridge University Press
Abstract
The goal of this work is to generate large statistically representative datasets to train machine learning models for disruption prediction provided by data from few existing discharges. Such a comprehensive training database is important to achieve satisfying and reliable prediction results in artificial neural network classifiers. Here, we aim for a robust augmentation of the training database for multivariate time series data using Student-t process regression. We apply Student-t process regression in a state space formulation via Bayesian filtering to tackle challenges imposed by outliers and noise in the training data set and to reduce the computational complexity. Thus, the method can also be used if the time resolution is high. We use an uncorrelated model for each dimension and impose correlations afterwards via coloring transformations. We demonstrate the efficacy of our approach on plasma diagnostics data of three different disruption classes from the DIII-D tokamak. To evaluate if the distribution of the generated data is similar to the training data, we additionally perform statistical analyses using methods from time series analysis, descriptive statistics, and classic machine learning clustering algorithms.
Description
Submitted for publication in Journal of Plasma Physics
MIT Department
Massachusetts Institute of Technology. Plasma Science and Fusion Center
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1017/s0022377822000769
