Continuous Convolutional Neural Networks for Disruption Prediction in Nuclear Fusion Plasmas (Papers Track)

William F Arnold (KAIST); Lucas Spangher (MIT PSFC); Cristina Rea (MIT PSFC)

Paper PDF Poster File Recorded Talk NeurIPS 2023 Poster Cite
Time-series Analysis Hybrid Physical 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.

Recorded Talk (direct link)