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Grid decarbonization for climate change requires dispatchable carbon-freeenergy like nuclear fusion. The tokamak concept offers a promising path forfusion, but one of the foremost challenges in implementation is the occurrenceof energetic plasma disruptions. In this study, we delve into Machine Learningapproaches to predict plasma state outcomes. Our contributions are twofold: (1)We present a novel application of Continuous Convolutional Neural Networks fordisruption prediction and (2) We examine the advantages and disadvantages ofcontinuous models over discrete models for disruption prediction by comparingour model with the previous, discrete state of the art, and show thatcontinuous models offer significantly better performance (Area Under theReceiver Operating Characteristic Curve = 0.974 v.s. 0.799) with fewerparameters

Continuous Convolutional Neural Networks for Disruption Prediction in Nuclear Fusion Plasmas