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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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
Proposes a decentralized contraction framework that certifies large-signal stability, exponential convergence, and explicit transient bounds for heterogeneous grid-forming inverters.
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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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A Unified Framework for Contraction Stability Analysis of Heterogeneous Grid-Forming Inverters
Proposes a decentralized contraction framework that certifies large-signal stability, exponential convergence, and explicit transient bounds for heterogeneous grid-forming inverters.