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#reinforcement learning Open access Sep 2026

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

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 simulat...

Ming-Long Wang, Chen-Guang Wan, Yue-Hang Wang et al. · 0 citations
Open access Jul 2026

Accelerating Edge Turbulence Simulations: A Physics-Informed Data-Driven Closure Model for the Hasegawa-Wakatani System

Accurate and efficient simulation of edge plasma turbulence is critical for predicting confinement in fusion devices, yet Direct Numerical Simulations (DNS) remain computationally expensive. To address this bottleneck, we present a physics-informed data-driven closure scheme demonstrated on the Hasegawa-Wakatani (HW)...

Kun-Peng Li, Youngwoo Cho, X. Garbet et al. · 0 citations

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