% IMPORTANT: The following is UTF-8 encoded. This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.
@INPROCEEDINGS{Cordes:911607,
author = {Cordes, Jakob and Tordeux, Antoine and Schadschneider,
Andreas and Rüdiger, Babara and Ugurcan, Baris},
title = {{N}oise-induced breakdown in linear self-driven particle
systems},
school = {Universität zu Köln},
reportid = {FZJ-2022-04866},
year = {2022},
abstract = {Self-driven particle systems can describe many
self-organized phenomena. Prominentexamples are oscillation,
wave, lane, or band formations, among other swarming
andcoordinated movements. Collective dynamics are
classically understood in the literature of active matter as
motility-induced phase separation using meta-stable
non-linear in-teraction models. Critical settings of the
parameters separate the disorder states from non-uniform
dynamics describing macroscopic patterns and structures. We
show in this contribution that noise effects can initiate
the spontaneous formation of waves insingle-file motions of
self-driven particles. In contrast to usual modeling
approaches, no non-linear interaction mechanisms or phase
transitions are necessary to coordinate the dynamics. The
stochastic effects initiate the self-organization in the
second order in a purely linear and ergodic framework. The
coupling of the noise to a discrete gradient in space yields
the emergence of stop-and-go waves, which we characterize
using speed and spacing auto-correlation functions. Varying
the characteristics of the noise allows for obtaining rich
dynamics ranging from coupled dynamics and stable
homogeneous dynamics to stop-and-go patterns with
deterministic oscillating features.},
month = {Aug},
date = {2022-08-22},
organization = {JMC 2022, Lyon (France), 22 Aug 2022 -
26 Aug 2022},
subtyp = {After Call},
cin = {IAS-7},
cid = {I:(DE-Juel1)IAS-7-20180321},
pnm = {5111 - Domain-Specific Simulation Data Life Cycle Labs
(SDLs) and Research Groups (POF4-511) / DFG project
446168800 - Multi-Agent-Modellierung der Dynamik von dichten
Fußgängermengen: Vorhersagen Verstehen (446168800)},
pid = {G:(DE-HGF)POF4-5111 / G:(GEPRIS)446168800},
typ = {PUB:(DE-HGF)24},
url = {https://juser.fz-juelich.de/record/911607},
}