A shared graph neural network framework jointly solves ACOPF and SCUC problems using physics constraints and shows improved generalization to unseen grid topologies.
Goal: A generalist combinatorial optimiza- tion agent learner.arXiv preprint arXiv:2406.15079
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2026 2verdicts
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LUMINA derives three design principles for physics-informed foundation models that balance accuracy, constraint satisfaction, and reliability on topology-transferable ACOPF problems.
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Towards Systematic Generalization for Power Grid Optimization Problems
A shared graph neural network framework jointly solves ACOPF and SCUC problems using physics constraints and shows improved generalization to unseen grid topologies.
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LUMINA: Foundation Models for Topology Transferable ACOPF
LUMINA derives three design principles for physics-informed foundation models that balance accuracy, constraint satisfaction, and reliability on topology-transferable ACOPF problems.