Skip to main content
Log in

Complete Cluster Predictability of the Cucker–Smale Flocking Model on the Real Line

  • Published:
Archive for Rational Mechanics and Analysis Aims and scope Submit manuscript

Abstract

We present the complete predictability of clustering for the Cucker–Smale (C–S) model on the line. Emergence of multi-cluster flocking is often observed in numerical simulations for the C–S model with short-range interactions. However, the explicit computation of the number of emergent multi-clusters a priori is a challenging problem for the Cucker–Smale flocking model. In this paper, we present an explicit criterion and algorithm to calculate the number of clusters and their bulk velocities in terms of initial configuration, coupling strength and communication weight function in a one-dimensional setting. We present a finite increasing sequence of coupling strengths in which the number of asymptotic clusters has a jump. We also provide several numerical examples and compare them with analytical results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Ahn S., Choi H., Ha S.-Y., Lee H.: On the collision avoiding initial-configurations to the Cucker–Smale type flocking models. Commun. Math. Sci. 10, 625–643 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  2. Ahn S., Ha S.-Y.: Stochastic flocking dynamics of the Cucker–Smale model with multiplicative white noises. J. Math. Phys. 51, 103301 (2010)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  3. Bellomo N., Ha S.-Y.: A quest toward a mathematical theory of the dynamics of swarms. Math. Models Methods Appl. Sci. 27, 745–770 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  4. Bolley F., Canizo J.A., Carrillo J.A.: Stochastic mean-field limit: non-Lipschitz forces and swarming. Math. Models Methods Appl. Sci. 21, 2179–2210 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  5. Canizo J.A., Carrillo J.A., Rosado J.: A well-posedness theory in measures for some kinetic models of collective motion. Math. Models Methods Appl. Sci. 21, 515–539 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  6. Carrillo J.A., D’ Orsogna M.R., Panferov V.: Double milling in self-propelled swarms from kinetic theory. Kinet. Relat. Models 2, 363–378 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  7. Carrillo J.A., Fornasier M., Rosado J., Toscani G.: Asymptotic flocking dynamics for the kinetic Cucker–Smale model. SIAM J. Math. Anal. 42, 218–236 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  8. Carrillo J.A., Klar A., Martin S., Tiwari S.: Self-propelled interacting particle systems with roosting force. Math. Models Methods Appl. Sci. 20, 1533–1552 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  9. Cho J., Ha S.-Y., Huang F., Jin C., Ko D.: Emergence of bi-cluster flocking for the Cucker–Smale model. Math. Models Methods Appl. Sci. 26, 1191–1218 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  10. Cho J., Ha S.-Y., Huang F., Jin C., Ko D.: Emergence of bi-cluster flocking for agent-based models with unit speed constraint. Anal. Appl. 14, 1–35 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  11. Cucker F., Dong J.-G.: Avoiding collisions in flocks. IEEE Trans. Autom. Control 55, 1238–1243 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  12. Cucker F., Mordecki E.: Flocking in noisy environments. J. Math. Pure Appl. 89, 278–296 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  13. Cucker F., Smale S.: Emergent behavior in flocks. IEEE Trans. Automat. Contr. 52, 852–862 (2007)

    Article  MATH  Google Scholar 

  14. Degond P., Motsch S.: Macroscopic limit of self-driven particles with orientation interaction. C. R. Math. Acad. Sci. Paris 345, 555–560 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  15. Degond P., Motsch S.: Large-scale dynamics of the persistent Turing Walker model of fish behavior. J. Stat. Phys. 131, 989–1022 (2008)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  16. Degond P., Motsch S.: Continuum limit of self-driven particles with orientation interaction. Math. Models Methods Appl. Sci. 18, 1193–1215 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  17. Duan R., Fornasier M., Toscani G.: A kinetic flocking model with diffusion. Commun. Math. Phys. 300, 95–145 (2010)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  18. Fornasier M., Haskovec J., Toscani G.: Fluid dynamic description of flocking via Povzner–Boltzmann equation. Phys. D 240, 21–31 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  19. Ha S.-Y., Ha T., Kim J.: Asymptotic flocking dynamics for the Cucker–Smale model with the Rayleigh friction. J. Phys. A Math. Theor. 43, 315201 (2010)

    Article  MATH  Google Scholar 

  20. Ha S.-Y., Ko D., Zhang Y.: Critical coupling strength of the Cucker–Smale model for flocking. Math. Models Methods Appl. Sci. 27, 1051–1087 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  21. Ha S.-Y., Lee K., Levy D.: Emergence of time-asymptotic flocking in a stochastic Cucker–Smale system. Commun. Math. Sci. 7, 453–469 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  22. Ha S.-Y., Liu J.-G.: A simple proof of Cucker–Smale flocking dynamics and mean field limit. Commun. Math. Sci. 7, 297–325 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  23. Ha, S.-Y., Park, J., Zhang, X.: On the first-order reduction of the Cucker–Smale model and its clustering dynamics (submitted)

  24. Ha S.-Y., Slemrod M.: Flocking dynamics of a singularly perturbed oscillator chain and the Cucker–Smale system. J. Dyn. Differ. Equ. 22, 325–330 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  25. Ha S.-Y., Tadmor E.: From particle to kinetic and hydrodynamic description of flocking. Kinet. Relat. Models 1, 415–435 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  26. Hendrickx M., Tsitsiklis J.N.: Convergence of type-symmetric and cut-balanced consensus seeking systems. IEEE Trans. Autom. Control 58, 214–218 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  27. Kuramoto Y.: International symposium on mathematical problems in mathematical physics. Lect. Notes Theor. Phys. 30, 420 (1975)

    Article  ADS  Google Scholar 

  28. Leonard N.E., Paley D.A., Lekien F., Sepulchre R., Fratantoni D.M., Davis R.E.: Collective motion, sensor networks and ocean sampling. Proc. IEEE 95, 48–74 (2007)

    Article  Google Scholar 

  29. Li Z., Xue X.: Cucker–Smale flocking under rooted leadership with fixed and switching topologies. SIAM J. Appl. Math. 70, 3156–3174 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  30. Motsch S., Tadmor E.: Heterophilious dynamics enhances consensus. SIAM Rev. 56, 577–621 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  31. Motsch S., Tadmor E.: A new model for self-organized dynamics and its flocking behavior. J. Stat. Phys. 144, 923–947 (2011)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  32. Perea L., Elosegui P., Gómez G.: Extension of the Cucker–Smale control law to space flight formation. J. Guid. Control Dyn. 32, 526–536 (2009)

    Article  ADS  Google Scholar 

  33. Paley D.A., Leonard N.E., Sepulchre R., Grunbaum D., Parrish J.K.: Oscillator models and collective motion. IEEE Control Syst. 27, 89–105 (2007)

    Google Scholar 

  34. Park J., Kim H., Ha S.-Y.: Cucker–Smale flocking with inter-particle bonding forces. IEEE Tran. Autom. Control 55, 2617–2623 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  35. Poyato D., Soler J.: Euler-type equations and commutators in singular and hyperbolic limits of kinetic Cucker–Smale models. Math. Models Methods Appl. Sci., 6, 1089–1152 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  36. Shen J.: Cucker–Smale flocking under hierarchical leadership. SIAM J. Appl. Math. 68, 694–719 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  37. Toner J., Tu Y.: Flocks, herds, and schools: A quantitative theory of flocking. Phys. Rev. E 58, 4828–4858 (1998)

    Article  ADS  MathSciNet  Google Scholar 

  38. Topaz C.M., Bertozzi A.L.: Swarming patterns in a two-dimensional kinematic model for biological groups. SIAM J. Appl. Math. 65, 152–174 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  39. Vicsek T., Czirók A., Ben-Jacob E., Cohen I., Schochet O.: Novel type of phase transition in a system of self-driven particles. Phys. Rev. Lett. 75, 1226–1229 (1995)

    Article  ADS  MathSciNet  Google Scholar 

  40. Winfree A. T.: Biological rhythms and the behavior of populations of coupled oscillators. J. Theor. Biol. 16, 15–42 (1967)

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the anonymous referee for his/her careful reading and constructive comments on the original manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jinyeong Park.

Additional information

Communicated by T.-P. Liu

The work of S.-Y. Ha is partially supported by the Samsung Science and Technology Foundation under Project (Number SSTF-BA1401-03) and the work of J. Kim was supported by the German Research Foundation (DFG) under project number IRTG 2235 and the work of J. Park was supported by the research fund of Hanyang University (HY-2018). The work of X. Zhang is supported by Scientific Research Foundation of Huazhong University of Science and Technology.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ha, SY., Kim, J., Park, J. et al. Complete Cluster Predictability of the Cucker–Smale Flocking Model on the Real Line. Arch Rational Mech Anal 231, 319–365 (2019). https://doi.org/10.1007/s00205-018-1281-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00205-018-1281-x

Navigation