Machine Learning for Electron-Scale Turbulence Modeling in W7-X
Researchers have developed machine learning models to predict electron-scale turbulence in the Wendelstein 7-X stellarator. These models use physics-guided scaling laws to estimate heat flux based on plasma parameters like temperature gradient and electron-to-ion temperature ratio. The models demonstrate strong predictive accuracy, comparable to detailed simulations, though a single radius-independent model was insufficient, indicating geometry-dependent physics not yet captured. AI
IMPACT This research could accelerate plasma physics simulations, aiding fusion energy development.