Neural net modeling of equilibria in NSTX-U

TL;DR

This paper develops neural networks for fast equilibrium and shape control modeling in the NSTX-U, enabling real-time plasma scenario prediction, with improved performance over existing methods.

Contribution

Introduces two neural networks, Eqnet and Pertnet, for equilibrium and plasma response prediction, enhancing real-time modeling capabilities for NSTX-U.

Findings

Eqnet can perform EFIT-like reconstructions using magnetic diagnostics.

The forward-mode Eqnet offers performance improvements over RTEFIT.

All neural networks demonstrated strong, reliable performance in tests.

Abstract

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 the Gspert code and predicts the non-rigid plasma response, a nonlinear term that arises in shape control modeling. The NNs are trained with different combinations of inputs and outputs in order to offer flexibility in use cases. In particular, Eqnet can use magnetic diagnostics as inputs and act as an EFIT-like reconstruction algorithm, or, by using pressure and current profile information the NN can act as a forward Grad-Shafranov equilibrium solver. This forward-mode version is envisioned to be implemented in the suite of tools for simulation of plasma scenarios. The reconstruction-mode version gives some performance improvements compared to the online reconstruction code real-time EFIT (RTEFIT), especially when vessel eddy currents are significant. We report strong performance for all NNs indicating that the models could reliably be used within closed-loop simulations or other applications. Some limitations are discussed.