Our paper on learning coherent clusters in weakly-connected network systems [1] has been accepted to the 5th Annual Learning for Dynamics and Control Conference. Congrats Hancheng!
[Bibtex] [Abstract] [Download PDF]
We propose a structure-preserving model-reduction methodology for large-scale dynamic networks with tightly-connected components. First, the coherent groups are identified by a spectral clustering algorithm on the graph Laplacian matrix that models the network feedback. Then, a reduced network is built, where each node represents the aggregate dynamics of each coherent group, and the reduced network captures the dynamic coupling between the groups. We provide an upper bound on the approximation error when the network graph is randomly generated from a weight stochastic block model. Finally, numerical experiments align with and validate our theoretical findings.
@inproceedings{mm2023l4dc,
abstract = {We propose a structure-preserving model-reduction methodology for large-scale dynamic networks with tightly-connected components. First, the coherent groups are identified by a spectral clustering algorithm on the graph Laplacian matrix that models the network feedback. Then, a reduced network is built, where each node represents the aggregate dynamics of each coherent group, and the reduced network captures the dynamic coupling between the groups. We provide an upper bound on the approximation error when the network graph is randomly generated from a weight stochastic block model. Finally, numerical experiments align with and validate our theoretical findings.},
author = {Min, Hancheng and Mallada, Enrique},
booktitle = {Proceedings of The 5th Annual Learning for Dynamics and Control Conference},
editor = {Matni, Nikolai and Morari, Manfred and Pappas, George J.},
grants = {CAREER-1752362, TRIPODS-1934979, CPS-2136324},
month = {6},
pages = {1167--1179},
publisher = {PMLR},
record = {presented, accepted Mar 2022, submitted Nov 2022},
series = {Proceedings of Machine Learning Research},
title = {Learning Coherent Clusters in Weakly-Connected Network Systems},
url = {https://mallada.ece.jhu.edu/pubs/2023-L4DC-MM.pdf},
volume = {211},
year = {2023}
}