Abstract
Systems showing collective motion are partially described by a distribution of positions and a distribution of velocities. While models of collective motion often focus on system features governed mostly by velocity distributions, the model presented in this paper also incorporates features influenced by positional distributions. A significant feature, the size of the largest connected component of the graph induced by the particle positions and their perception range, is identified using a 1-d self-propelled particle model (SPP). Based on largest connected components, properties of the system dynamics are found that are time-invariant. A simplified macroscopic model can be defined based on this time-invariance, which may allow for simple, concise, and precise predictions of systems showing collective motion.
| Originalsprache | Englisch |
|---|---|
| Titel | ANTS 2018: Swarm Intelligence |
| Seitenumfang | 12 |
| Band | 11172 |
| Herausgeber (Verlag) | Springer Verlag |
| Erscheinungsdatum | 03.10.2018 |
| Seiten | 290-301 |
| ISBN (Print) | 978-3-030-00532-0 |
| ISBN (elektronisch) | 978-3-030-00533-7 |
| DOIs | |
| Publikationsstatus | Veröffentlicht - 03.10.2018 |
| Veranstaltung | 11th International Conference on Swarm Intelligence - Rome, Italien Dauer: 29.10.2018 → 31.10.2018 Konferenznummer: 219989 |
UN SDGs
Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung
-
SDG 9 – Industrie, Innovation und Infrastruktur
Fingerprint
Untersuchen Sie die Forschungsthemen von „The Role of Largest Connected Components in Collective Motion“. Zusammen bilden sie einen einzigartigen Fingerprint.Zitieren
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver