activity
20212026
most citedReinforcement Learning Optimizes Power Dispatch in Decentralized Power Grid

7 citations · 9 across the 4 of their papers we have counts for

collaborators

5 papers

physics.soc-ph2026

A Panoramic Network Overview of Shortcut Impact on Power Grids

Yunju Choi, Daekyung Lee, Heetae Kim

Synchronization of coupled oscillators on complex networks underlies critical functions in natural and engineered systems, including power-grid operation. While synchronization sta…

cond-mat.stat-mech2026

Phase lag enhances synchronization in coupled oscillators with inertia

Sudo Yi, Cook Hyun Kim, Heetae Kim +1

The second-order Kuramoto model with inertia exhibits different dynamical behaviors than the first-order KM without inertia. A central difference is its lower synchronization due t…

physics.soc-ph2024★ 1 cited

Kaleidoscopic reorganization of network communities across different scales

Wonhee Jeong, Daekyung Lee, Heetae Kim +1

The notion of structural heterogeneity is pervasive in real networks, and their community organization is no exception. Still, a vast majority of community detection methods assume…

physics.soc-ph2024★ 7 cited

Reinforcement Learning Optimizes Power Dispatch in Decentralized Power Grid

Yongsun Lee, Hoyun Choi, Laurent Pagnier +6

Effective frequency control in power grids has become increasingly important with the increasing demand for renewable energy sources. Here, we propose a novel strategy for resolvin…

physics.data-an2021★ 1 cited

Self-consistent gravity model for inferring node mass in flow networks

Daekyung Lee, Wonguk Cho, Heetae Kim +3

The gravity model, inspired by Newton's law of universal gravitation, has long served as a primary tool for interpreting trade flows between countries, using a country's economic `…