Reconstruction of the reversed field pinch magnetic perturbations in toroidal geometry by means of Deep Learning for real-time plasma control

In view of the forthcoming operation of RFX-mod2 in reversed field pinch configuration, a significant enhancement for the experiment's real-time control system is transitioning from the conventional simplified cylindrical approximation to the correct toroidal reconstruction. Presently the toroidal reconstruction starting from the experimental measurements, requires lengthy calculations that are impractical in real-time. Given that the solution space is reasonably well-behaved, the solving algorithm can be substituted with a Deep Learning (DL) network that can be integrated into the real-time system. This thesis tackles the first step of these complex calculations, which is the computation of the equilibrium in toroidal geometry. To achieve this, different neural networks have been trained and validated leveraging the existing toroidal reconstruction code. The most promising model has been selected and a series of optimizations, including pruning and heterogeneous quantization, have been performed in order to adapt the network to a real-time environment built in FPGA components. This work serves as a proof of concept for the feasibility of the real-time toroidal reconstruction using DL methods.