Phase autoencoder for limit-cycle oscillators

Autor:	Yawata, Koichiro, Fukami, Kai, Taira, Kunihiko, Nakao, Hiroya
Rok vydání:	2024
Předmět:	Nonlinear Sciences - Adaptation and Self-Organizing Systems Computer Science - Machine Learning Nonlinear Sciences - Chaotic Dynamics
Druh dokumentu:	Working Paper
Popis:	We present a phase autoencoder that encodes the asymptotic phase of a limit-cycle oscillator, a fundamental quantity characterizing its synchronization dynamics. This autoencoder is trained in such a way that its latent variables directly represent the asymptotic phase of the oscillator. The trained autoencoder can perform two functions without relying on the mathematical model of the oscillator: first, it can evaluate the asymptotic phase and phase sensitivity function of the oscillator; second, it can reconstruct the oscillator state on the limit cycle in the original space from the phase value as an input. Using several examples of limit-cycle oscillators, we demonstrate that the asymptotic phase and phase sensitivity function can be estimated only from time-series data by the trained autoencoder. We also present a simple method for globally synchronizing two oscillators as an application of the trained autoencoder. Comment: 12 pages, 16 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2403.06992 Zobrazit plný text záznamu View this record from Arxiv