Machine learning of turbulent transport in fusion plasmas with neural network

Abstract

Turbulent transport resulting from drift waves, typically, the ion temperature gradient (ITG) mode and trapped electron mode (TEM), is of great significance in magnetic confinement fusion. It is also well known that turbulence simulation is a challenging issue in both the complex physical model and huge CPU cost as well as long computation time. In this work, a credible turbulence transport prediction model, extended fluid code (ExFC-NN), based on a neural network (NN) approach is established using simulation data by performing an ExFC, in which multi-scale multi-mode fluctuations, such as ITG and TEM turbulence are involved. Results show that the characteristics of turbulent transport can be successfully predicted including the type of dominant turbulence and the radial averaged fluxes under any set of local gradient parameters. Furthermore, a global NN model can well reproduce the radial profiles of turbulence perturbation intensities and fluxes much faster than existing codes. A large number of comparative predictions show that the newly constructed NN model can realize rapid experimental analysis and provide reference data for experimental parameter design in the future.