| PHYSICS OF GASES, PLASMAS, AND ELECTRIC DISCHARGES |
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Simulation Prediction of Heat Transport with Machine Learning in Tokamak Plasmas |
| Hui Li1*, Yan-Lin Fu2, Ji-Quan Li3, and Zheng-Xiong Wang1* |
1Key Laboratory of Materials Modification by Laser, Ion, and Electron Beams (Ministry of Education), School of Physics, Dalian University of Technology, Dalian 116024, China
2State 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
3Southwestern Institute of Physics, Chengdu 610041, China
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| Cite this article: |
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Hui Li, Yan-Lin Fu, Ji-Quan Li et al 2023 Chin. Phys. Lett. 40 125201 |
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Abstract 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 first-principles 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.
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Received: 06 July 2023
Published: 17 December 2023
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