3 papers
physics.plasm-ph2026
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…
physics.plasm-ph2026
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…
physics.plasm-ph2025
FGE: A Fast Free-Boundary Grad-Shafranov Evolutive Solver
Cosmas Heiß, 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…