Data Augmentation for Disruption Prediction via Robust Surrogate Models

MCML Authors

Katharina Röck (née Rath)

Dr.

* Former Member

→ Group Bernd Bischl
 Statistical Learning and Data Science

David Rügamer

Prof. Dr.

Principal Investigator

Statistics, Data Science and Machine Learning

Bernd Bischl

Prof. Dr.

Director

Statistical Learning and Data Science

Abstract

The goal of this work is to generate large statistically representative data sets 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 colouring 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.

article

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Journal of Plasma Physics

88.5. Oct. 2022.

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Authors

K. Röck • D. Rügamer • B. Bischl • U. von Toussaint • C. Rea • A. Maris • R. Granetz • C. G. Albert

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Links

DOI

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Research Area

 A1 | Statistical Foundations & Explainability