Kinetic description of swarming dynamics with topological interaction and transient leaders

TL;DR

This paper develops a kinetic model for bird flocking that incorporates transient leaders and topological interactions, introducing a stochastic particle method to efficiently simulate large-scale collective motion in 2D and 3D.

Contribution

It presents a novel kinetic framework with a stochastic particle simulation method that reduces computational complexity for large flock systems with transient leadership.

Findings

Efficient simulation of large flocks using $k$-nearest neighbors search.

Validation of the model through numerical experiments in 2D and 3D.

Demonstration of collective dynamics influenced by transient leaders.

Abstract

In this paper, we present a model describing the collective motion of birds. The model introduces spontaneous changes in direction which are initialized by few agents, here referred as leaders, whose influence act on their nearest neighbors, in the following referred as followers. Starting at the microscopic level, we develop a kinetic model that characterizes the behaviour of large flocks with transient leadership. One significant challenge lies in managing topological interactions, as identifying nearest neighbors in extensive systems can be computationally expensive. To address this, we propose a novel stochastic particle method to simulate the mesoscopic dynamics and reduce the computational cost of identifying closer agents from quadratic to logarithmic complexity using a $k$-nearest neighbours search algorithm with a binary tree. Lastly, we conduct various numerical experiments for different scenarios to validate the algorithm's effectiveness and investigate collective dynamics in both two and three dimensions.