28 citations · 28 across the 1 of their papers we have counts for
3 papers
physics.plasm-ph2021★ 28 cited
Scenario adaptive disruption prediction study for next generation burning-plasma tokamaks
J. Zhu, C. Rea, R. S. Granetz +10
Next generation high performance (HP) tokamaks risk damage from unmitigated disruptions at high current and power. Achieving reliable disruption prediction for a device's HP operat…
physics.plasm-ph2020
Hybrid deep learning architecture for general disruption prediction across tokamaks
J. X. Zhu, C. Rea, K. Montes +3
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…
physics.plasm-ph2019
An application of survival analysis to disruption prediction via Random Forests
R. A. Tinguely, K. J. Montes, C. Rea +2
One of the most pressing challenges facing the fusion community is adequately mitigating or, even better, avoiding disruptions of tokamak plasmas. However, before this can be done,…