  A network community-based reduced-order model is developed to capture key
interactions amongst coherent structures in high-dimensional unsteady vortical
flows. The present approach is data-inspired and founded on network-theoretic
techniques to identify important vortical communities that are comprised of
vortical elements that share similar dynamical behavior. The overall
interaction-based physics of the high-dimensional flow field is distilled into
the vortical community centroids, considerably reducing the system dimension.
Taking advantage of these vortical interactions, the proposed methodology is
applied to formulate reduced-order models for the inter-community dynamics of
vortical flows, and predict lift and drag forces on bodies in wake flows. We
demonstrate the capabilities of these models by accurately capturing the
macroscopic dynamics of a collection of discrete point vortices, and the
complex unsteady aerodynamic forces on a circular cylinder and an airfoil with
a Gurney flap. The present formulation is found to be robust against simulated
experimental noise and turbulence due to its integrating nature of the system
reduction.
