Neural net modeling of equilibria in NSTX-U. (arXiv:2202.13915v2 [physics.plasm-ph] UPDATED)

Neural networks (NNs) offer a path towards synthesizing and interpreting data
on faster timescales than traditional physics-informed computational models. In
this work we develop two neural networks relevant to equilibrium and shape
control modeling, which are part of a suite of tools being developed for the
National Spherical Torus Experiment-Upgrade (NSTX-U) for fast prediction,
optimization, and visualization of plasma scenarios. The networks include
Eqnet, a free-boundary equilibrium solver trained on the EFIT01 reconstruction
algorithm, and Pertnet, which is trained on …

arxiv equilibria modeling physics