Implementation of AI/Deep Learning Disruption Predictor into a Plasma Control System

This paper reports on advances to the state-of-the-art deep-learning disruption prediction models based on the Fusion Recurrent Neural Network (FRNN) originally introduced a 2019 Nature publication. In particular, the predictor now features not only the disruption score, as an indicator of the proba...

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Veröffentlicht in:	arXiv.org 2022-04
Hauptverfasser:	Tang, William, Ge Dong, Barr, Jayson, Erickson, Keith, Conlin, Rory, Boyer, M Dan, Kates-Harbeck, Julian, Felker, Kyle, Rea, Cristina, Logan, Nikolas C, Svyatkovskiy, Alexey, Feibush, Eliot, Abbatte, Joseph, Mitchell, Clement, Grierson, Brian, Nazikian, Raffi, Lin, Zhihong, Eldon, David, Moser, Auna, Maslov, Mikhail
Format:	Artikel
Sprache:	eng
Schlagworte:	Actuators Channels Control systems Deep learning Disruption Joint European Torus Physics - Computational Physics Physics - Plasma Physics Plasma Plasma control Prediction models Real time Recurrent neural networks Safety factors Sensitivity
Online-Zugang:	Volltext
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