Continuous Convolutional Neural Networks for Disruption Prediction in Nuclear Fusion Plasmas
This work addresses disruption prediction for nuclear fusion energy systems, which is critical for grid decarbonization, but it is incremental as it builds on existing machine learning approaches with a novel model type.
The paper tackled the problem of predicting plasma disruptions in nuclear fusion tokamaks, a key challenge for fusion energy, by applying Continuous Convolutional Neural Networks and achieved significantly better performance with an AUC of 0.974 compared to 0.799 for previous discrete models.
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