Hybrid deep-learning architecture for general disruption prediction across multiple tokamaks

@article{Zhu2020HybridDA,
  title={Hybrid deep-learning architecture for general disruption prediction across multiple tokamaks},
  author={J.X. Zhu and Cristina Rea and K. J. Montes and R S Granetz and R M Sweeney and R. A. Tinguely},
  journal={Nuclear Fusion},
  year={2020},
  volume={61}
}
In this paper, we present a new deep-learning disruption-prediction algorithm based on important findings from explorative data analysis which effectively allows knowledge transfer from existing devices to new ones, thereby predicting disruptions using very limited disruption data from the new devices. The explorative data analysis, conducted via unsupervised clustering techniques confirms that time-sequence data are much better separators of disruptive and non-disruptive behavior than the… 

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