Development of a neural network model for peeling-ballooning stability analysis in the KSTAR tokamak pedestals

Abstract The neural network model, MISHKA-NN is developed to mitigate the computational burden associated with the linear ideal MHD stability analysis of the pedestal based on the peeling-ballooning model. By utilizing both 1D plasma profiles (current density, pressure gradient, and safety factor) and 0D parameters (plasma geometry, total current, and toroidal mode number), the model predicts linear growth rate of edge-localized ideal MHD instability in a given equilibrium state. By enabling the prediction of each instability within a second, the model reduces the time required for plotting a pedestal peeling-ballooning stability diagram ($j-\alpha$ diagram) from approximately 100 CPU hours to a few CPU minutes. Notably, even with the utilization of parametric pressure and current profiles and plasma boundary shapes for the training dataset, the model shows a satisfactory level of performance in benchmarking the $j-\alpha$ diagram for the reconstructed equilibrium from a KSTAR tokamak experiment. We anticipate the model to serve as a versatile alternative to 2D linear MHD stability codes, alleviating numerical costs.