A data-driven model for stable long-horizon autoregressive prediction of plasma current and control-oriented boundary evolution in EAST

Abstract

Accurate long-horizon prediction of tokamak plasma current, position, and boundary evolution is essential for magnetic control, rapid controller development, and reinforcement-learning-based optimization. However, high-fidelity physics-based simulators are often computationally prohibitive when large numbers of simulations are required for controller tuning and large-scale optimization. In this work, we develop a fast, data-driven model for the Experimental Advanced Superconducting Tokamak (EAST) to predict plasma current, position, and shape evolution over horizons of up to 1 s, with an inference time of 0.1 s on a single NVIDIA H800 GPU. Evaluated on a temporally separated test set comprising 5907 discharges, the model demonstrates strong generalization to evolving operating conditions and maintains stable agreement with experimental measurements during long-horizon autoregressive rollouts. The mean geometric error of the plasma centroid and the control-oriented boundary representation is 2.4 cm, indicating reliable centimeter-level accuracy for control-oriented prediction of plasma position and shape evolution. The proposed framework provides a practical, high-throughput alternative to first-principles simulators, enabling efficient control algorithm prototyping, large-scale scenario exploration, and future data-driven optimization for tokamak plasma control. This study focuses on the post-shaping phase of EAST discharges, where sustained plasma current and control-oriented boundary regulation are most critical.