000911607 001__ 911607
000911607 005__ 20230310131318.0
000911607 0247_ $$2Handle$$a2128/32750
000911607 037__ $$aFZJ-2022-04866
000911607 041__ $$aEnglish
000911607 1001_ $$0P:(DE-Juel1)187329$$aCordes, Jakob$$b0$$eCorresponding author$$ufzj
000911607 1112_ $$aJMC 2022$$cLyon$$d2022-08-22 - 2022-08-26$$wFrance
000911607 245__ $$aNoise-induced breakdown in linear self-driven particle systems
000911607 260__ $$c2022
000911607 3367_ $$033$$2EndNote$$aConference Paper
000911607 3367_ $$2BibTeX$$aINPROCEEDINGS
000911607 3367_ $$2DRIVER$$aconferenceObject
000911607 3367_ $$2ORCID$$aCONFERENCE_POSTER
000911607 3367_ $$2DataCite$$aOutput Types/Conference Poster
000911607 3367_ $$0PUB:(DE-HGF)24$$2PUB:(DE-HGF)$$aPoster$$bposter$$mposter$$s1669202054_4739$$xAfter Call
000911607 502__ $$cUniversität zu Köln
000911607 520__ $$aSelf-driven particle systems can describe many self-organized phenomena. Prominentexamples are oscillation, wave, lane, or band formations, among other swarming andcoordinated movements. Collective dynamics are classically understood in the literature of active matter as motility-induced phase separation using meta-stable non-linear in-teraction models. Critical settings of the parameters separate the disorder states from non-uniform dynamics describing macroscopic patterns and structures. We show in this contribution that noise effects can initiate the spontaneous formation of waves insingle-file motions of self-driven particles. In contrast to usual modeling approaches, no non-linear interaction mechanisms or phase transitions are necessary to coordinate the dynamics. The stochastic effects initiate the self-organization in the second order in a purely linear and ergodic framework. The coupling of the noise to a discrete gradient in space yields the emergence of stop-and-go waves, which we characterize using speed and spacing auto-correlation functions. Varying the characteristics of the noise allows for obtaining rich dynamics ranging from coupled dynamics and stable homogeneous dynamics to stop-and-go patterns with deterministic oscillating features.
000911607 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation  Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
000911607 536__ $$0G:(GEPRIS)446168800$$aDFG project 446168800 - Multi-Agent-Modellierung der Dynamik von dichten Fußgängermengen: Vorhersagen  Verstehen (446168800)$$c446168800$$x1
000911607 7001_ $$0P:(DE-Juel1)159135$$aTordeux, Antoine$$b1
000911607 7001_ $$0P:(DE-HGF)0$$aSchadschneider, Andreas$$b2
000911607 7001_ $$0P:(DE-HGF)0$$aRüdiger, Babara$$b3
000911607 7001_ $$0P:(DE-HGF)0$$aUgurcan, Baris$$b4
000911607 8564_ $$uhttps://juser.fz-juelich.de/record/911607/files/Poster_JMC_Cordes.pdf$$yOpenAccess
000911607 909CO $$ooai:juser.fz-juelich.de:911607$$pdriver$$pVDB$$popen_access$$popenaire
000911607 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)187329$$aForschungszentrum Jülich$$b0$$kFZJ
000911607 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5111$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
000911607 9141_ $$y2022
000911607 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000911607 920__ $$lyes
000911607 9201_ $$0I:(DE-Juel1)IAS-7-20180321$$kIAS-7$$lZivile Sicherheitsforschung$$x0
000911607 980__ $$aposter
000911607 980__ $$aVDB
000911607 980__ $$aUNRESTRICTED
000911607 980__ $$aI:(DE-Juel1)IAS-7-20180321
000911607 9801_ $$aFullTexts