شبكة عصبية تقرأ مستقبل البلازما سريع

الملخص الأصلي

كما كتبه المؤلفون، بالإنجليزية arXiv:2602.19110

Accurately predicting plasma behavior based on discharge configurations is essential for the safe and efficient operation of tokamak experiments. While physics-based integrated modeling codes provide valuable insights, their high computational cost limits their applicability for fast scenario design and control optimization. In this study, we propose a transformer-based machine learning model to predict key global plasma parameters on the WEST tokamak, including the normalized beta ($$\beta_{n}$$), toroidal beta ($$\beta_{t}$$), poloidal beta ($$\beta_{p}$$), plasma stored energy ($$W_{\mathrm{mhd}}$$), safety factor at the magnetic axis ($$q_{0}$$), and safety factor at the 95% flux surface ($$q_{95}$$). The model uses only signals that can be defined before the discharge, such as magnetic coil currents, auxiliary heating power, plasma current reference, and line-averaged plasma density. Trained on 550 discharges from the WEST campaigns, the model demonstrates an average mean square error (MSE) loss of 0.026, an average coefficient of determination $$R^{2}$$ of 0.94, and achieves inference times on the order of 0.1 seconds. These results highlight the potential of data-driven surrogate models for assisting in discharge planning, scenario evaluation, and real-time control of tokamak plasmas.