Elasticity-Based Mechanism for the Collective Motion of Self-Propelled Particles with Springlike Interactions: A Model System for Natural and Artificial Swarms

Ferrante, Eliseo; Turgut, Ali Emre; Dorigo, Marco; Huepe, Cristián

doi:10.1103/physrevlett.111.268302

Cited by 112 publications

(105 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By constantly converting external energy into directed motion, such systems of self-propelled particles can be driven into a non-equilibrium steady state. Steady dynamic states of orientationally ordered collective motion can arise [10][11][12][13][14][15][16][17][18][19], but also swirling and turbulent-like situations were observed [20][21][22][23]. There are different possibilities how to provide the energy input in experiments: light, if sufficiently strong, can create a temperature gradient leading to self-thermophoresis [24,25], or lead to the local demixing of a binary solvent [26].…”

Section: Introductionmentioning

confidence: 99%

Dynamical mean-field theory and weakly non-linear analysis for the phase separation of active Brownian particles

Speck

Menzel

Bialké

et al. 2015

The Journal of Chemical Physics

138

195

View full text Add to dashboard Cite

Recently, we have derived an effective Cahn-Hilliard equation for the phase separation dynamics of active Brownian particles by performing a weakly non-linear analysis of the effective hydrodynamic equations for density and polarization [Phys. Rev. Lett. 112, 218304 (2014)]. Here we develop and explore this strategy in more detail and show explicitly how to get to such a large-scale, meanfield description starting from the microscopic dynamics. The effective free energy emerging from this approach has the form of a conventional Ginzburg-Landau function. On the coarsest scale, our results thus agree with the mapping of active phase separation onto that of passive fluids with attractive interactions through a global effective free energy (mobility-induced phase transition). Particular attention is paid to the square-gradient term necessary for the dynamics. We finally discuss results from numerical simulations corroborating the analytical results.

show abstract

Section: Introductionmentioning

confidence: 99%

Dynamical mean-field theory and weakly non-linear analysis for the phase separation of active Brownian particles

Speck

Menzel

Bialké

et al. 2015

The Journal of Chemical Physics

138

195

View full text Add to dashboard Cite

show abstract

“…In this paper, Hemelrijk & Hildenbrandt [196] used a detailed model of the motion rules of starlings and found that SF correlations were observed even when far from any Figure 5. A self-organized flocking process that starts in a disordered state (left) and eventually converges to a parallel motion state (adapted from Ferrante et al [195]). This system was shown to exhibit scale-free correlations due to the propagation of collective modes in the work by Huepe et al [194].…”

Section: Collective Mode Dynamicsmentioning

confidence: 99%

“…Huepe et al [194], for example, postulate this mechanism as the source of the SF correlations measured for speed fluctuations. They proposed a simple position-based model that describes an idealized active elastic system in which largescale collective oscillations (corresponding here to the low-mass modes) dominate the dynamics [195] (see figure 5 for an example of possible dynamics induced by this system). They then showed in numerical simulations that the presence of these collective oscillations of the fundamental elastic modes produces scale-invariant correlations of the angular and speed fluctuations in active systems.…”

Section: Collective Mode Dynamicsmentioning

confidence: 99%

Scale invariance in natural and artificial collective systems: a review

Khaluf

Ferrante

Simoens

et al. 2017

J. R. Soc. Interface.

Self Cite

View full text Add to dashboard Cite

Self-organized collective coordinated behaviour is an impressive phenomenon, observed in a variety of natural and artificial systems, in which coherent global structures or dynamics emerge from local interactions between individual parts. If the degree of collective integration of a system does not depend on size, its level of robustness and adaptivity is typically increased and we refer to it as scale-invariant. In this review, we first identify three main types of self-organized scale-invariant systems: scale-invariant spatial structures, scale-invariant topologies and scale-invariant dynamics. We then provide examples of scale invariance from different domains in science, describe their origins and main features and discuss potential challenges and approaches for designing and engineering artificial systems with scale-invariant properties.

show abstract

“…Several researches have reported collective motions resulting from attraction-repulsion interaction without the alignment252627282930. For example, active Brownian agents that interact globally with each other by attraction show noise-induced transition between translational and rotational motions2526.…”

mentioning

confidence: 99%

Emergence of a coherent and cohesive swarm based on mutual anticipation

Murakami

Niizato

Gunji

2017

Sci Rep

View full text Add to dashboard Cite

Collective behavior emerging out of self-organization is one of the most striking properties of an animal group. Typically, it is hypothesized that each individual in an animal group tends to align its direction of motion with those of its neighbors. Most previous models for collective behavior assume an explicit alignment rule, by which an agent matches its velocity with that of neighbors in a certain neighborhood, to reproduce a collective order pattern by simple interactions. Recent empirical studies, however, suggest that there is no evidence for explicit matching of velocity, and that collective polarization arises from interactions other than those that follow the explicit alignment rule. We here propose a new lattice-based computational model that does not incorporate the explicit alignment rule but is based instead on mutual anticipation and asynchronous updating. Moreover, we show that this model can realize densely collective motion with high polarity. Furthermore, we focus on the behavior of a pair of individuals, and find that the turning response is drastically changed depending on the distance between two individuals rather than the relative heading, and is consistent with the empirical observations. Therefore, the present results suggest that our approach provides an alternative model for collective behavior.

show abstract

Elasticity-Based Mechanism for the Collective Motion of Self-Propelled Particles with Springlike Interactions: A Model System for Natural and Artificial Swarms

Cited by 112 publications

References 37 publications

Dynamical mean-field theory and weakly non-linear analysis for the phase separation of active Brownian particles

Dynamical mean-field theory and weakly non-linear analysis for the phase separation of active Brownian particles

Scale invariance in natural and artificial collective systems: a review

Emergence of a coherent and cohesive swarm based on mutual anticipation

Contact Info

Product

Resources

About