1 citations · 1 across the 9 of their papers we have counts for
10 papers
Scaling Laws for Physics-Aware ACOPF Surrogate Learning
Yijiang Li, Emon Dey, Stefano Fenu +3
Learning-based surrogates for AC optimal power flow (ACOPF) promise large speedups over classical solvers, but their operational value depends on physical feasibility as much as pr…
UNION: A Unified AC-OPF Framework for Topology-Varying Real-Time Grid Operation
Kyungnam Park, Keunju Song, Yeji Lim +3
Secure real-time grid operation requires fast AC optimal power flow (AC-OPF) tools that stay accurate and feasible as operating conditions and topology change. Learning-based metho…
FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Training
Yijiang Li, Emon Dey, Zilinghan Li +3
Federated learning (FL) across multiple HPC facilities faces stochastic admission delays from batch schedulers that dominate wall-clock time. Synchronous FL suffers from severe str…
Scalable Heterogeneous Graph Foundation Models for Data-Driven Optimal Power Flow in Smart Grids
Massimiliano Lupo Pasini, Yijiang Li, Kibaek Kim +1
Fast and reliable optimal power flow (OPF) approximation is important for power system operation, yet heterogeneous OPF graph models are often evaluated either with architecture-sp…
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…
Towards Systematic Generalization for Power Grid Optimization Problems
Zeeshan Memon, Yijiang Li, Hongwei Jin +2
AC Optimal Power Flow (ACOPF) and Security-Constrained Unit Commitment (SCUC) are fundamental optimization problems in power system operations. ACOPF serves as the physical backbon…
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…
FedSpaLLM: Federated Pruning of Large Language Models
Guangji Bai, Yijiang Li, Zilinghan Li +2
Large Language Models (LLMs) achieve state-of-the-art performance but are challenging to deploy due to their high computational and storage demands. Pruning can reduce model size,…
Modeling and solving cascading failures across interdependent infrastructure systems
Yijiang Li, Kibaek Kim, Sven Leyffer +3
Physical infrastructure systems supply crucial resources to residential, commercial, and industrial activities. These infrastructure systems generally consist of multiple types of…
SparseLLM: Towards Global Pruning for Pre-trained Language Models
Guangji Bai, Yijiang Li, Chen Ling +2
The transformative impact of large language models (LLMs) like LLaMA and GPT on natural language processing is countered by their prohibitive computational demands. Pruning has eme…