Transferable Cross-Tokamak Disruption Prediction with Deep Hybrid Neural Network Feature Extractor

作     者：Zheng, Wei Xue, Fengming Zhang, Ming Chen, Zhongyong Shen, Chengshuo Ai, Xinkun Wang, Nengchao Chen, Dalong Guo, Bihao Ding, Yonghua Chen, Zhipeng Yang, Zhoujun Shen, Biao Xiao, Bingjia Pan, Yuan 

作者机构：International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan430074 China Institute of Plasma Physics HFIPS Chinese Academy of Sciences Hefei230031 China College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518060 China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2022年

核心收录：

主　　题：Forecasting 

摘      要：Predicting disruptions across different tokamaks is a great obstacle to overcome. Future tokamaks can hardly tolerate disruptions at high performance discharge. Few disruption discharges at high performance can hardly compose an abundant training set, 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 disruption prediction model containing a feature extractor that is able to extract common disruption precursor traces in tokamak diagnostic data, and a transferable disruption classifier. Based on the concerns above, the paper first presents a deep fusion feature extractor designed specifically for extracting disruption precursor features from common diagnostics on tokamaks according to currently known precursors of disruption, providing a promising foundation for transferable models. The fusion feature extractor is proved by comparing with manual feature extraction on J-TEXT. Based on the feature extractor trained on J-TEXT, the disruption prediction model was transferred to EAST data with mere 20 discharges from EAST experiment. The performance is comparable with a model trained with 1896 discharges from EAST. From the comparison among other model training scenarios, transfer learning showed its potential in predicting disruptions across different tokamaks. Copyright © 2022, The Authors. All rights reserved.