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.
Czechoslovak Mathematical Journal , volume =
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
PhySwarm combines a multi-phase advection-diffusion-reaction density model with an equivalent microscopic motion model and a neural-physics controller trained via RL-PINN to generate and control multi-stage emergent behaviors in robot swarms.
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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Physics-Informed Modeling and Control of Emergent Behaviors in Robot Swarms
PhySwarm combines a multi-phase advection-diffusion-reaction density model with an equivalent microscopic motion model and a neural-physics controller trained via RL-PINN to generate and control multi-stage emergent behaviors in robot swarms.