Interaction network effects on position- and velocity-based models of collective motion

2020-08-01
Turgut, Ali Emre
Okay, Ilkin Ege
Ferrante, Eliseo
Huepe, Cristian
We study how the structure of the interaction network affects self-organized collective motion in two minimal models of self-propelled agents: the Vicsek model and the Active-Elastic (AE) model. We perform simulations with topologies that interpolate between a nearest-neighbour network and random networks with different degree distributions to analyse the relationship between the interaction topology and the resilience to noise of the ordered state. For the Vicsek case, we find that a higher fraction of random connections with homogeneous or power-law degree distribution increases the critical noise, and thus the resilience to noise, as expected due to small-world effects. Surprisingly, for the AE model, a higher fraction of random links with power-law degree distribution can decrease this resilience, despite most links being long-range. We explain this effect through a simple mechanical analogy, arguing that the larger presence of agents with few connections contributes localized low-energy modes that are easily excited by noise, thus hindering the collective dynamics. These results demonstrate the strong effects of the interaction topology on self-organization. Our work suggests potential roles of the interaction network structure in biological collective behaviour and could also help improve decentralized swarm robotics control and other distributed consensus systems.
JOURNAL OF THE ROYAL SOCIETY INTERFACE

Suggestions

Computational modeling of passive myocardium
Göktepe, Serdar; Wong, Jonathan; Kuhl, Ellen (Wiley, 2011-01-01)
This work deals with the computational modeling of passive myocardial tissue within the framework ofmixed, non-linear finite element methods. We consider a recently proposed, convex, anisotropic hyperelastic model that accounts for the locally orthotropic micro-structure of cardiac muscle. A coordinate-free representation of anisotropy is incorporated through physically relevant invariants of the Cauchy-Green deformation tensors and structural tensors of the corresponding material symmetry group. This model...
Numerical simulation of the effect of time-to-loading on peri-implant bone
AKÇA, KIVANÇ; Eer, Atilim; Canay, Senay (Elsevier BV, 2010-01-01)
Purpose: To evaluate the effect of time-to-loading on trabecular bone around single-tooth dental implants using numerical solutions based on computer models.
Hybrid wavelet-neural network models for time series data
Kılıç, Deniz Kenan; Uğur, Ömür; Department of Financial Mathematics (2021-3-3)
The thesis aims to combine wavelet theory with nonlinear models, particularly neural networks, to find an appropriate time series model structure. Data like financial time series are nonstationary, noisy, and chaotic. Therefore using wavelet analysis helps better modeling in the sense of both frequency and time. S&P500 (∧GSPC) and NASDAQ (∧ IXIC) data are divided into several components by using multiresolution analysis (MRA). Subsequently, each part is modeled by using a suitable neural network structure. ...
Error analysis and assessment of unsteady forces acting on a flapping wing micro air vehicle: free flight versus wind-tunnel experimental methods
Caetano, J. V.; Perçin, Mustafa; van Oudheusden, B. W.; Remes, B.; de Wagter, C.; de Croon, G. C. H. E.; de Visser, C. C. (IOP Publishing, 2015-10-01)
An accurate knowledge of the unsteady aerodynamic forces acting on a bio-inspired, flapping-wing micro air vehicle (FWMAV) is crucial in the design development and optimization cycle. Two different types of experimental approaches are often used: determination of forces from position data obtained from external optical tracking during free flight, or direct measurements of forces by attaching the FWMAV to a force transducer in a wind-tunnel. This study compares the quality of the forces obtained from both m...
Comparison of two inference approaches in Gaussian graphical models
Purutçuoğlu Gazi, Vilda; Wit, Ernst (Walter de Gruyter GmbH, 2017-04-01)
Introduction: The Gaussian Graphical Model (GGM) is one of the well-known probabilistic models which is based on the conditional independency of nodes in the biological system. Here, we compare the estimates of the GGM parameters by the graphical lasso (glasso) method and the threshold gradient descent (TGD) algorithm.
Citation Formats
A. E. Turgut, I. E. Okay, E. Ferrante, and C. Huepe, “Interaction network effects on position- and velocity-based models of collective motion,” JOURNAL OF THE ROYAL SOCIETY INTERFACE, pp. 0–0, 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/45902.