Deep neural network Grad-Shafranov solver constrained with measured magnetic signals
Semin Joung, Jaewook Kim, Sehyun Kwak, J. G. Bak, S. G. Lee, H. S. Han, H. S. Kim, Geunho Lee, Daeho Kwon, Y. -c. Ghim
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A neural network solving Grad-Shafranov equation constrained with measured magnetic signals to reconstruct magnetic equilibria in real time is developed. Database created to optimize the neural network's free parameters contain off-line EFIT results as the output of the network from 1,118 KSTAR experimental discharges of two different campaigns. Input data to the network constitute magnetic signals measured by a Rogowski coil (plasma current), magnetic pick-up coils (normal and tangential components of magnetic fields) and flux loops (poloidal magnetic fluxes). The developed neural networks fully reconstruct not only the poloidal flux function ( R, Z) but also the toroidal current density function j_( R, Z) with the off-line EFIT quality. To preserve robustness of the networks against a few missing input data, an imputation scheme is utilized to eliminate the required additional training sets with large number of possible combinations of the missing inputs.