|
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.
|
|
Notes
| PSFC REPORT PSFC/JA-22-9
The present contribution is supported by the Helmholtz Association of German Research Centers under the joint research school HIDSS-0006 "Munich School for Data Science - MUDS" (KR) and the MIT-Germany Lockheed Martin Seed Fund (KR, CR, AM, UvT). This work has been carried out within the framework of the EUROfusion Consortium, funded by the European Union via the Euratom Research and Training Programme (Grant Agreement No 101052200 - EUROfusion). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them (CA). This work was supported by the Federal Ministry of Education and Research (BMBF) of Germany by Grant No. 01IS18036A (DR, BB). This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Fusion Energy Sciences, using the DIII-D National Fusion Facility, a DOE Office of Science user facility, under Award(s) DE- SC0014264, and DE-FC02-04ER54698.
If this record does not contain the full text, then the manuscript has been embargoed by the publisher thus restricting open access for 12 to 24 months after publication. |
