A machine learning approach based on generative topographic mapping for disruption prevention and avoidance at JET
Author:
Funder
H2020 Euratom
Publisher
IOP Publishing
Subject
Condensed Matter Physics,Nuclear and High Energy Physics
Link
http://iopscience.iop.org/article/10.1088/1741-4326/ab2ea9/pdf
Reference42 articles.
1. Implementing a finite-state off-normal and fault response system for disruption avoidance in tokamaks
2. Survey of disruption causes at JET
3. The impact of the ITER-like wall at JET on disruptions
4. Disruptions in ITER and strategies for their control and mitigation
5. A First Analysis of JET Plasma Profile-Based Indicators for Disruption Prediction and Avoidance
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