Overview of machine learning applications in fusion plasma experiments on J-TEXT tokamak

Abstract

Abstract Machine learning research and applications in fusion plasma experiments are one of the main subjects on J-TEXT. Since 2013, various kinds of traditional machine learning, as well as deep learning methods have been applied to fusion plasma experiments. Further applications in the real-time experimental environment have proved the feasibility and effectiveness of the methods. For disruption prediction, we started by predicting disruptions of limited classes with a short warning time that could not meet the requirements of the mitigation system. After years of study, nowadays disruption prediction methods on J-TEXT are able to predict all kinds of disruptions with a high success rate and long enough warning time. Furthermore, cross-device disruption prediction methods have obtained promising results. Interpretable analysis of the models are studied. For diagnostics data processing, efforts have been made to reduce manual work in processing and to increase the robustness of the diagnostic system. Models based on both traditional machine learning and deep learning have been applied to real-time experimental environments. The models have been cooperating with the plasma control system and other systems, to make joint decisions to further support the experiments.