AI Article Synopsis
- The phase autoencoder is designed to encode the asymptotic phase of limit-cycle oscillators, which is crucial for understanding their synchronization.
- The autoencoder is trained to directly represent the asymptotic phase and can evaluate both the phase value and the phase sensitivity function solely from time-series data, bypassing traditional mathematical models.
- Additionally, it can reconstruct the oscillator's state and enables a technique for globally synchronizing two oscillators using the learned phase information.
Article Abstract
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 the 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 the 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.
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| http://dx.doi.org/10.1063/5.0205718 | DOI Listing |
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