Coarse-grained analysis of extreme-scale fish school

Collective behavior is ubiquitous in animal groups. In fish schools, global patterns emerge from individual-level behavioral rules and flow interactions. Here, we employed a model of schooling fish based on data-inferred behavioral rules and all-to-all far field inviscid hydrodynamic interaction with 2D dipole model. With the aid of high-performance parallel computing, we studied the emergent collective patterns in large schools of the order of 10⁴ individuals. We found that the structures which emerge globally at lower number of fish (10-100), like milling, schooling, or turning, breakdown with increasing school size. Instead, the school dynamically scatters and reassembles into local structures with rich dynamics and polarization properties. Our preliminary efforts to analyze these dynamics indicate surprising interplay between fish density and local order. These findings pave the way towards creating a novel data-driven framework for describing extreme active matter with free boundaries.