Reconstructing the Plasma Boundary with a Reduced Set of Diagnostics

Abstract

This study investigates the feasibility of reconstructing the last closed flux surface (LCFS) in the DIII-D tokamak using neural network models trained on reduced input feature sets, addressing an ill-posed task. Two models are compared: one trained solely on coil currents and another incorporating coil currents, plasma current, and loop voltage. The model trained exclusively on coil currents achieved a mean point displacement of 0.04 m on a held-out test set, while the inclusion of plasma current and loop voltage reduced the error to 0.03 m. This comparison highlights the trade-offs between input feature complexity and reconstruction accuracy, demonstrating the potential of machine learning algorithms to perform effectively in data-limited environments, such as those expected in Fusion Power Plants (FPP) due to diagnostic constraints imposed by the presence of blankets and shielding.