Diffusion prior for KSTAR equilibrium reconstruction under sensor dropout
Abstract
We study equilibrium reconstruction for the Korea Superconducting Tokamak Advanced Research (KSTAR) device under magnetic sensor dropout, with a diffusion model as the prior. Magnetic measurements leave most components of the toroidal current density Jϕ undetermined, and sensor loss leaves more of them to the prior. The diffusion model learns only Jϕ, conditioned on the coil currents, the plasma current and the product of major radius and toroidal field, which do not depend on the dropout. The physics enters through a linear forward operator built from sensor response matrices of the LIUQE code. Because the observations are linear, a variant of decoupled annealing posterior sampling fits them with a closed-form linear correction. On measured signals of 37 KSTAR shots, we switch off a random fraction (0.0 to 0.9, ten settings) of the 124 magnetic channels in use and compare with LIUQE under the same masks, taking the full-sensor LIUQE reconstruction as the label. Both methods fit the remaining channels to a similar level. The median relative distance of Jϕ from the label stays at 3.22--4.07\% for the diffusion reconstruction in all settings, whereas that of LIUQE reaches 11.89\% at dropout 0.9. At dropout 0.5--0.9, the diffusion reconstruction is closer to the label on 27--37 of the 37 shots. At low dropout, LIUQE is closer on most shots. For the derived flux, the diffusion reconstruction is closer on 21--36 shots at dropout 0.5--0.9, and its error at high dropout comes from the vessel current estimate, not from the prior.
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BibTeX
@misc{nam2026diffusion,
title = {{Diffusion prior for KSTAR equilibrium reconstruction under sensor dropout}},
author = {Hyungkeun Nam and Jaemin Seo},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.36536},
eprint = {2609.36536},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.36536},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Diffusion prior for KSTAR equilibrium reconstruction under sensor dropout
AU - Hyungkeun Nam
AU - Jaemin Seo
PY - 2026
DA - 2026/09/29
PB - arXiv
DO - 10.48550/arxiv.2609.36536
UR - https://arxiv.org/abs/2609.36536
AB - We study equilibrium reconstruction for the Korea Superconducting Tokamak Advanced Research (KSTAR) device under magnetic sensor dropout, with a diffusion model as the prior. Magnetic measurements leave most components of the toroidal current density J_ϕ undetermined, and sensor loss leaves more of them to the prior. The diffusion model learns only J_ϕ, conditioned on the coil currents, the plasma current and the product of major radius and toroidal field, which do not depend on the dropout. The physics enters through a linear forward operator built from sensor response matrices of the LIUQE code. Because the observations are linear, a variant of decoupled annealing posterior sampling fits them with a closed-form linear correction. On measured signals of 37 KSTAR shots, we switch off a random fraction (0.0 to 0.9, ten settings) of the 124 magnetic channels in use and compare with LIUQE under the same masks, taking the full-sensor LIUQE reconstruction as the label. Both methods fit the remaining channels to a similar level. The median relative distance of J_ϕ from the label stays at 3.22--4.07\% for the diffusion reconstruction in all settings, whereas that of LIUQE reaches 11.89\% at dropout 0.9. At dropout 0.5--0.9, the diffusion reconstruction is closer to the label on 27--37 of the 37 shots. At low dropout, LIUQE is closer on most shots. For the derived flux, the diffusion reconstruction is closer on 21--36 shots at dropout 0.5--0.9, and its error at high dropout comes from the vessel current estimate, not from the prior.
ER -