Predicting disruptions across different tokamaks is necessary for next generation device.Future large-scale tokamaks can hardly tolerate disruptions at high performance discharge,which makes it difficult for current d...Predicting disruptions across different tokamaks is necessary for next generation device.Future 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 problem.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 tokamaks.Based 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 tokamaks.Furthermore,an FFE-based disruption predictor on J-TEXT is demonstrated.The feature extractor is aimed to extracting disruption-related precursors and is designed according to the precursors of disruption and their representations in common tokamak diagnostics.Strong inductive bias on tokamak diagnostics data is introduced.The 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 method.Results 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.展开更多
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 applie...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.展开更多
基金Project supported by the National Key R&D Program of China (Grant No. 2022YFE03040004)the National Natural Science Foundation of China (Grant No. 51821005)
文摘Predicting disruptions across different tokamaks is necessary for next generation device.Future 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 problem.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 tokamaks.Based 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 tokamaks.Furthermore,an FFE-based disruption predictor on J-TEXT is demonstrated.The feature extractor is aimed to extracting disruption-related precursors and is designed according to the precursors of disruption and their representations in common tokamak diagnostics.Strong inductive bias on tokamak diagnostics data is introduced.The 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 method.Results 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.
基金supported by the National Key R&D Program of China(No.2022YFE03040004)National Natural Science Foundation of China(No.51821005)
文摘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.
