Learning Coarse-Grained Dynamics on Graph
<|reference_start|>Learning Coarse-Grained Dynamics on Graph: We consider a Graph Neural Network (GNN) non-Markovian modeling framework to identify coarse-grained dynamical systems on graphs. Our main idea is to systematically determine the GNN architecture by inspecting how the leading term of the Mori-Zwanzig memory term depends on the coarse-grained interaction coefficients that encode the graph topology. Based on this analysis, we found that the appropriate GNN architecture that will account for $K$-hop dynamical interactions has to employ a Message Passing (MP) mechanism with at least $2K$ steps. We also deduce that the memory length required for an accurate closure model decreases as a function of the interaction strength under the assumption that the interaction strength exhibits a power law that decays as a function of the hop distance. Supporting numerical demonstrations on two examples, a heterogeneous Kuramoto oscillator model and a power system, suggest that the proposed GNN architecture can predict the coarse-grained dynamics under fixed and time-varying graph topologies.<|reference_end|>
@article{yu2024learning,
    title={Learning Coarse-Grained Dynamics on Graph},
    author={Yin Yu, John Harlim, Daning Huang, and Yan Li},
    journal={arXiv preprint arXiv:2405.09324},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.09324},
    primaryClass={math.NA cond-mat.dis-nn cs.LG cs.NA}
}