Accelerated Bayesian inference of plasma profiles with self-consistent MHD equilibria at W7-X via neural networks

Abstract

High-β operations require a fast and robust inference of plasma parameters with a self-consistent magnetohydrodynamic (MHD) equilibrium. Precalculated MHD equilibria are usually employed at Wendelstein 7-X (W7-X) due to the high computational cost. To address this, we couple a physics-regularized artificial neural network (NN) model that approximates the ideal-MHD equilibrium with the Bayesian modeling framework Minerva. We show the fast and robust inference of plasma profiles (electron temperature and density) with a self-consistent MHD equilibrium approximated by the NN model. We investigate the robustness of the inference across diverse synthetic W7-X plasma scenarios. The inferred plasma parameters and their uncertainties are compatible with the parameters inferred using the variational moments equilibrium code (VMEC), and the inference time is reduced by more than two orders of magnitude. This work suggests that MHD self-consistent inferences of plasma parameters can be performed between shots.