Neural-network emulators trained on roughly 100,000 to 1,000,000 Grad-Shafranov equilibria can generate virtual circuits with 5-10% displacement accuracy, and shaping currents can be inferred online with few-Ampere residuals.
Real-time feedback control of βp based on deep reinforcement learning on EAST
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Real-Time Applicability of Emulated Virtual Circuits for Tokamak Plasma Shape Control
Neural-network emulators trained on roughly 100,000 to 1,000,000 Grad-Shafranov equilibria can generate virtual circuits with 5-10% displacement accuracy, and shaping currents can be inferred online with few-Ampere residuals.