Introduces graph-to-image prediction of per-node dynamic stability landscapes in oscillator networks from topology, releases two 10k-graph datasets, and shows GNN-CNN models achieve good accuracy with cross-size generalization.
Proceedings of the National Academy of Sciences , volume =
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Develops a convex optimization method using graph Laplacians and linear matrix inequalities to minimize expected synchronization cost in lossless power networks, validated on the IEEE 30-bus test system with reported reductions in transients.
citing papers explorer
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Learning Dynamic Stability Landscapes in Synchronization Networks
Introduces graph-to-image prediction of per-node dynamic stability landscapes in oscillator networks from topology, releases two 10k-graph datasets, and shows GNN-CNN models achieve good accuracy with cross-size generalization.
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Minimizing the Expected Cost of Synchronization in Lossless Power Networks
Develops a convex optimization method using graph Laplacians and linear matrix inequalities to minimize expected synchronization cost in lossless power networks, validated on the IEEE 30-bus test system with reported reductions in transients.