On reduced inertial PDE models for Cucker-Smale flocking dynamics
Sebastian Zimper, Federico Cornalba, Nata\v{s}a Djurdjevac Conrad, Ana, Djurdjevac

TL;DR
This paper introduces a new reduced PDE model for Cucker-Smale flocking that depends only on particle position, providing a direct link to particle dynamics and offering analytical and numerical insights.
Contribution
The paper presents a novel reduced inertial PDE model for flocking that is not derived from the mean-field limit but directly reduces empirical densities at the particle level.
Findings
The reduced PDE satisfies a natural flocking definition.
Quantification of the discrepancy between PDE and particle system in specific cases.
Numerical simulations support the theoretical analysis.
Abstract
In particle systems, flocking refers to the phenomenon where particles' individual velocities eventually align. The Cucker-Smale model is a well-known mathematical framework that describes this behavior. Many continuous descriptions of the Cucker-Smale model use PDEs with both particle position and velocity as independent variables, thus providing a full description of the particles mean-field limit (MFL) dynamics. In this paper, we introduce a novel reduced inertial PDE model consisting of two equations that depend solely on particle position. In contrast to other reduced models, ours is not derived from the MFL, but directly includes the model reduction at the level of the empirical densities, thus allowing for a straightforward connection to the underlying particle dynamics. We present a thorough analytical investigation of our reduced model, showing that: firstly, our reduced PDE…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsDistributed Control Multi-Agent Systems · Guidance and Control Systems
