A real-time machine learning-based disruption predictor in DIII-D
Author:
Funder
Office of Science
Publisher
IOP Publishing
Subject
Condensed Matter Physics,Nuclear and High Energy Physics
Link
http://iopscience.iop.org/article/10.1088/1741-4326/ab28bf/pdf
Reference32 articles.
1. A cross-tokamak neural network disruption predictor for the JET and ASDEX Upgrade tokamaks
2. Unbiased and non-supervised learning methods for disruption prediction at JET
3. An advanced disruption predictor for JET tested in a simulated real-time environment
4. Automatic disruption classification in JET with the ITER-like wall
5. A reduced resistive wall mode kinetic stability model for disruption forecasting
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