5 papers
SABLE: GPU-Based Power Flow Accelerator for Sparsity-Aware Batched Learning
Suho Park, Keunju Song, Hongseok Kim
Recent studies have developed GPU-based approaches for solving AC power flow and successfully applied them to standalone power flow problems. However, integrating these approaches…
Physics-Informed Graph Learning Acceleration for Large-Scale AC-OPF with Topology Changes
Keunju Song, Kyungnam Park, Sua Choi +5
In power systems, alternating current optimal power flow (AC-OPF) has been a challenging problem for decades due to its nonconvexity, but fast and efficient solutions are even more…
LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learning
Hongwei Jin, Keunju Song, Zeeshan Memon +5
AC optimal power flow (ACOPF) is foundational yet computationally expensive in power grid operations, driving learning-based surrogates for large-scale grid analysis. These surroga…
LUMINA: Foundation Models for Topology Transferable ACOPF
Yijiang Li, Zeeshan Memon, Hongwei Jin +7
Foundation models in general promise to accelerate scientific computation by learning reusable representations across problem instances, yet constrained scientific systems, where p…
Fixed Point Neural Acceleration and Inverse Surrogate Model for Battery Parameter Identification
Hojin Cheon, Hyeongseok Seo, Jihun Jeon +3
The rapid expansion of electric vehicles has intensified the need for accurate and efficient diagnosis of lithium-ion batteries. Parameter identification of electrochemical battery…