Machine learning for disruption warnings on Alcator C-Mod, DIII-D, and EAST

This paper reports on disruption prediction using a shallow machine learning method known as a random forest, trained on large databases containing only plasma parameters that are available in real-time on Alcator C-Mod, DIII-D, and EAST. The database for each tokamak contains parameters sampled   1...

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Veröffentlicht in:	Nuclear fusion 2019-07, Vol.59 (9), p.96015
Hauptverfasser:	Montes, K.J., Rea, C., Granetz, R.S., Tinguely, R.A., Eidietis, N., Meneghini, O.M., Chen, D.L., Shen, B., Xiao, B.J., Erickson, K., Boyer, M.D.
Format:	Artikel
Sprache:	eng
Schlagworte:	70 PLASMA PHYSICS AND FUSION TECHNOLOGY control disruption machine learning prediction real-time stability tokamak
Online-Zugang:	Volltext
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