Continuous Convolutional Neural Networks for Disruption Prediction in
  Nuclear Fusion Plasmas
William F Arnold Lucas Spangher Christina Rea
Grid decarbonization for climate change requires dispatchable carbon-free
energy like nuclear fusion. The tokamak concept offers a promising path for
fusion, but one of the foremost challenges in implementation is the occurrence
of energetic plasma disruptions. In this study, we delve into Machine Learning
approaches to predict plasma state outcomes. Our contributions are twofold: (1)
We present a novel application of Continuous Convolutional Neural Networks for
disruption prediction and (2) We examine the advantages and disadvantages of
continuous models over discrete models for disruption prediction by comparing
our model with the previous, discrete state of the art, and show that
continuous models offer significantly better performance (Area Under the
Receiver Operating Characteristic Curve = 0.974 v.s. 0.799) with fewer
parameters