Machine learning prediction of plasma behavior from discharge configurations on WEST

Abstract 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 Tungsten (W) Environment in Steady-State Tokamak (WEST) tokamak, including the normalized beta ( β n ), toroidal beta ( β t ), poloidal beta ( β p ), plasma stored energy ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>W</mml:mi> <mml:mrow> <mml:mrow> <mml:mi>mhd</mml:mi> </mml:mrow> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> ), 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 s. These results highlight the potential of data-driven surrogate models for assisting in discharge planning, scenario evaluation, and real-time control of tokamak plasmas.