Simulation Prediction of Heat Transport with Machine Learning in Tokamak Plasmas

作     者：李慧 付艳林 李继全 王正汹 Hui Li;Yan-Lin Fu;Ji-Quan Li;Zheng-Xiong Wang

作者机构：Key Laboratory of Materials Modification by LaserIonand Electron Beams(Ministry of Education)School of PhysicsDalian University of TechnologyDalian 116024China State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational ChemistryDalian Institute of Chemical PhysicsChinese Academy of SciencesDalian 116023China Southwestern Institute of PhysicsChengdu 610041China 

出 版 物：《Chinese Physics Letters》 (中国物理快报（英文版）)

年 卷 期：2023年第40卷第12期

页      面：79-83页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 082701[工学-核能科学与工程] 0827[工学-核科学与技术] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.12205035  11925501  12275071  and U1967206) 

主　　题：process radial simplify 

摘      要：Machine learning opens up new possibilities for research of plasma confinement. Specifically, models constructed using machine learning algorithms may effectively simplify the simulation process. Previous firstprinciples simulations could provide physics-based transport information, but not fast enough for real-time applications or plasma control. To address this issue, this study proposes SExFC, a surrogate model of the Gyro-Landau Extended Fluid Code(ExFC). As an extended version of our previous model ExFC-NN, SExFC can capture more features of transport driven by the ion temperature gradient mode and trapped electron mode,using an extended database initially generated with ExFC simulations. In addition to predicting the dominant instability, radially averaged fluxes and radial profiles of fluxes, the well-trained SExFC may also be suitable for physics-based rapid predictions that can be considered in real-time plasma control systems in the future.