Byungnam Kahng
Distinguished Professor in Grid Modernization track
Department of Energy Engineering, Korea Institute of Energy Technology (KENTECH)
Contact
Email: bkahng@kentech.ac.kr
TEL: (+82) 061-320-9209
Research keywords
Target systems:
Complex power-grid systems and Physical Systems.
Subjects:
Stability of Power Grid systems, Phase transitions, Synchronization, Percolation, Disease contagion, etc.
Methods:
Network theory, Statistical Mechanics, Stochastic Process, Monte Carlo simulations, Nonlinear Dynamics, Machine Learning.
Related research field
Phase transitions and critical phenomena, AI Applications, Smart Grids and Electric Power Systems, Battery Materials and Systems, Equilibrium thermodynamics
Byungnam Kahng is a distinguished professor at the Korea Institute of Energy Technology (KENTECH), where he serves as the director of the Center for Complex Systems. Before joining KENTECH, he was a faculty member at Seoul National University and Konkuk University. He also completed postdoctoral research at the University of California, Berkeley. He earned his Ph.D. in Physics from Boston University, having previously obtained both his M.S. and B.S. in Physics from Seoul National University. His research focuses on phase transitions and critical phenomena in both equilibrium and non-equilibrium complex systems, including spin models, percolation, synchronization, and other related areas. Recently, he has become interested in the stability of power grid systems from the perspective of statistical physics.
Representative publications (Full paper list, see Google Scholar)
Explosive percolations:
- Hybrid percolation transition in cluster merging processes: continuously varying exponents, Physical Review Letters (PRL) (2016).
- Avoiding a spanning cluster in percolation models, Science (2013).
- Suppression effect on explosive percolation, PRL (2011).
- Percolation transitions in scale-free networks under the Achlioptas process, PRL (2009).
Structure of complex networks:
- Classification of scale-free networks, PNAS (2002).
Structural feature of networks:
- Skeleton and fractal scaling in complex networks, PRL (2006).
Dynamics on networks:
- First passage time for random walks in heterogeneous networks, PRL (2012).
Cascading failures:
- Sandpile on scale-free networks, PRL (2003).
- Prediction and mitigation of nonlocal cascading failures using graph neural networks, Chaos (2023).
- Reinforcement learning optimizes power dispatch in decentralized power grid, Chaos, Solitons, and Fractals (2024).
Transport on networks:
- Universal behavior of load distribution in scale-free networks, PRL (2001).
Research highlights
Power Dispatch with Reinforcement Learning (RL):
Inference and control in complex systems using machine learning approach
Lee, Yongsun, et al. "Reinforcement learning optimizes power dispatch in decentralized power grid." Chaos, Solitons & Fractals 186 (2024): 115293.
Cascading failures in complex systems:
Blackout in power-grid systems, Epidemic contagion in complex systems, Jamming transition of data packets on the Internet
B. Jhun, H. Choi, Y. Lee, J. Lee, C. H. Kim, and B. Kahng, Prediction and mitigation of nonlocal cascading failures using graph neural networks, Chaos 33, 013115 (2023).
Phases Transitions & Hybrid Phase Transitions:
Critical behaviors between continuous and discontinuous transitions such as percolation and synchronization transitions.