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An Inverse Grad-Shafranov Neural Network Approach to Tokamak Magnetic Control
Allen M. Wang, Adriano Mele, Cosmas Heiß +12
A new approach to tokamak magnetic control enabling high-precision plasma shaping and novel real-time adaptability is experimentally demonstrated on the Tokamak a Configuration Var…
Kinetic Equilibrium Prediction at TCV using RAPTOR and FBT
C. E. Contré, A. Merle, O. Sauter +13
We present results from a new Kinetic-Equilibrium Prediction (KEP) workflow and shot preparation for full TCV discharges, by coupling predict-first RAPTOR transport simulations wit…
Experimental validation of a fast control-oriented, physics-informed surrogate model for plasma equilibrium reconstruction in the TCV tokamak
M. Grandin, A. Mele, C. Galperti +5
Magnetic equilibrium reconstruction provides the plasma state estimate required for real-time shape control in tokamaks. We present a fast, physics-informed neural network surrogat…
FGE: A Fast Free-Boundary Grad-Shafranov Evolutive Solver
Cosmas HeiÃ, Antoine Merle, Francesco Carpanese +5
Accurate and rapid simulation of the free boundary tokamak plasma equilibrium evolution is essential for modern plasma control, stability analysis, and scenario development. This p…