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.
作者
李慧
付艳林
李继全
王正汹
Hui Li;Yan-Lin Fu;Ji-Quan Li;Zheng-Xiong Wang(Key Laboratory of Materials Modification by Laser,Ion,and Electron Beams(Ministry of Education),School of Physics,Dalian University of Technology,Dalian 116024,China;State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry,Dalian Institute of Chemical Physics,Chinese Academy of Sciences,Dalian 116023,China;Southwestern Institute of Physics,Chengdu 610041,China)
基金
supported by the National Natural Science Foundation of China(Grant Nos.12205035, 11925501, 12275071, and U1967206)。
