Models of dynamical systems
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Bayes_Kuramoto.py
README.md
kuramoto.py
requirements.txt

README.md

Dynamical systems

Kuramoto

Implementation of famous Kuramoto coupling model [1,2]. Current version (0.3) provides:

  • Harmonic coupling terms.
  • Perturbations of any type, with few predefied, can be activated.

Bayesian-Kuramoto

Class executing dynamical Bayesian inference of time-evolving coupled system in presence of noise [3,4]. Algorithm based on set of papers (e.g. see below) and MatLab code provided by one of the authors (http://py-biomedical.lancaster.ac.uk/).

References

[1] Kuramoto, Y. (1984). Chemical Oscillations, Waves, and Turbulence (Vol. 19). http://doi.org/10.1007/978-3-642-69689-3

[2] Acebron, J. A. et al (2005). The Kuramoto model: A simple paradigm for synchronization phenomena. Reviews of Modern Physics, 77(1), 137–185. http://doi.org/10.1103/RevModPhys.77.137

[3] A. Duggento et al., “Dynamical Bayesian inference of time-evolving interactions: From a pair of coupled oscillators to networks of oscillators,” Phys. Rev. E, 2012.

[4] Tomislav Stankovski et al., "A tutorial on time-evolving dynamical Bayesian inference" Eur. Phys. J. Special Topics 223, 2014.