activity
20232026
most citedEnhancing Disruption Prediction through Bayesian Neural Network in KSTAR

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

physics.plasm-ph2026

Mechanism of Ionization Avalanche in Tokamak Microwave Gas Breakdown

Jinwoo Gwak, Yeongsun Lee, Jeongwon Lee +5

Microwave breakdown driven by electron cyclotron (EC) waves provides a non-inductive route to plasma initiation in reactor-scale tokamaks. We introduce a three-dimensional Monte Ca…

physics.comp-ph2026

Extension of a multi-region free-surface MHD solver beyond the inductionless approximation

Min Ki Jung, Brian Wynne, Francisco Saenz +4

Free-surface liquid metal flows are a leading candidate for the plasma-facing components of future fusion reactors, but existing transient, three-dimensional, free-surface MHD solv…

physics.plasm-ph2026

Non-thermal electron cyclotron emission during runaway plateau in tokamak disruptions from a highly anisotropic dielectric tensor

Yeongsun Lee, Kikyung Park, Tchanou Park +3

During the runaway plateau phase in a tokamak, a cold background electron temperature of O(1 eV) forbids the onset of kinetic instability due to strong collisional damping. Neverth…

physics.plasm-ph2024

Multimodal Super-Resolution: Discovering hidden physics and its application to fusion plasmas

Azarakhsh Jalalvand, SangKyeun Kim, Jaemin Seo +6

A non-linear system governed by multi-spatial and multi-temporal physics scales cannot be fully understood with a single diagnostic, as each provides only a partial view, leading t…

physics.plasm-ph2023★ 1 cited

Enhancing Disruption Prediction through Bayesian Neural Network in KSTAR

Jinsu Kim, Jeongwon Lee, Jaemin Seo +3

Disruption in tokamak plasmas, stemming from various instabilities, poses a critical challenge, resulting in detrimental effects on the associated devices. Consequently, the proact…