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Data augmentation for disruption prediction via robust surrogate models

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Data augmentation for disruption prediction via robust surrogate models
 
Identifier https://doi.org/10.7910/DVN/FMJCAD
 
Creator Katharina Rath, David Rügamer, Bernd Bischl, Udo von Toussaint, Cristina Rea, Andrew Maris, Robert Granetz, Christopher G. Albert
 
Publisher Harvard Dataverse
 
Description 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.
 
Subject Physics
DIII-D
disruption prediction
machine learning
statistical analysis
surrogate models