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Disruption prediction based on fusion feature extractor on J-TEXT

Chinese Physics B 2023年第7期32卷 12-23页

作者：郑玮薛凤鸣陈忠勇沈呈硕艾鑫坤钟昱王能超张明丁永华陈志鹏杨州军潘垣 International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics State Key Laboratory of Advanced Electromagnetic Engineering and TechnologySchool of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074China Institute of Artificial Intelligence Huazhong University of Science and TechnologyWuhan 430074China

Predicting disruptions across different tokamaks is necessary for next generation *** large-scale tokamaks can hardly tolerate disruptions at high performance discharge,which makes it difficult for current data-driven methods to obtain an acceptable result.A machine learning method capable of transferring a disruption prediction model trained on one tokamak to another is required to solve the *** key is a feature extractor which is able to extract common disruption precursor traces in tokamak diagnostic data,and can be easily transferred to other *** on the concerns above,this paper presents a deep feature extractor,namely,the fusion feature extractor(FFE),which is designed specifically for extracting disruption precursor features from common diagnostics on ***,an FFE-based disruption predictor on J-TEXT is *** feature extractor is aimed to extracting disruption-related precursors and is designed according to the precursors of disruption and their representations in common tokamak *** inductive bias on tokamak diagnostics data is *** paper presents the evolution of the neural network feature extractor and its comparison against general deep neural networks,as well as a physics-based feature extraction with a traditional machine learning *** demonstrate that the FFE may reach a similar effect with physics-guided manual feature extraction,and obtain a better result compared with other deep learning methods.

关键词： feature extractor disruption prediction deep learning tokamak diagnostics

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