no code implementations • 18 Sep 2021 • J. Zhu, C. Rea, R. S. Granetz, E. S. Marmar, K. J. Montes, R. Sweeney, R. A. Tinguely, D. L. Chen, B. Shen, B. J. Xiao, D. Humphreys, J. Barr, O. Meneghini
Next generation high performance (HP) tokamaks risk damage from unmitigated disruptions at high current and power.
no code implementations • 2 Jul 2020 • J. X. Zhu, C. Rea, K. Montes, R. S. Granetz, R. Sweeney, R. A. Tinguely
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 disruptive data from the new devices.