2025/05/15 by M. S. Stokolesov, M.R. Nurgaliev, Stokolesov, M. S. +11
Engineering · Materials Science · Physics and Astronomy · #FOS: Physical sciences #Fusion materials and technologies #Magnetic confinement fusion research #Plasma Physics (physics.plasm-ph) #Superconducting Materials and Applications
paper · pdf · doi:10.48550/arxiv.2505.10709
openalex publication_date 2025/05/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.