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Haruma Furukawa

Publications and source records attributed to Haruma Furukawa.

2 recordsLinked to original sources

A Phase Description of Mutually Coupled Chaotic Oscillators

The synchronization of rhythms is ubiquitous in both natural and engineered systems, and the demand for data-driven analysis is growing. When rhythms arise from limit cycles, phase reduction theory shows that their dynamics are universally modeled as coupled phase oscillators under weak coupling. This simple representation enables direct inference of inter-rhythm coupling functions from measured time-series data. However, strongly rhythmic chaos can masquerade as noisy limit cycles. In such cases, standard estimators still return plausible coupling functions even though a phase-oscillator model lacks a priori justification. We therefore extend the phase description to the chaotic oscillators. Specifically, we derive a closed equation for the phase difference by defining the phase on a Poincaré section and averaging the phase dynamics over invariant measures of the induced return maps. Numerically, the derived theoretical functions are in close agreement with those inferred from time-series data. Consequently, our results justify the applicability of phase description to coupled chaotic oscillators and show that data-driven coupling functions retain clear dynamical meaning in the absence of limit cycles.

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Gaussian process regression with additive periodic kernels for two-body interaction analysis in coupled phase oscillators

Since many physical laws -- from classical mechanics to electromagnetism -- are formulated as two-body interactions, the same perspective naturally extends to biological and social dynamics. Here we focus on rhythmic phenomena, where phase reduction theory shows that synchronization dynamics can be universally described by coupled phase oscillators. Estimating the interaction functions of such systems from data offers a direct path to understanding and predicting such dynamics. Existing Fourier-series-based methods encounter difficulties with limited or biased data. To overcome this, we employ Gaussian process regression with additive periodic kernels. In our approach, we incorporate information about the estimation target into the statistical model in advance by designing kernel functions that capture the characteristics -- additivity and $2π$-periodicity -- of the coupling functions. Furthermore, owing to the Bayesian framework, our method enables the evaluation of uncertainty in the estimation results. We validate our approach on Van der Pol, FitzHugh-Nagumo, and spiking neural models. Our approach outperforms Fourier-series baselines in both error and stability under biased phase sampling. This enables data-driven studies of rhythm dynamics across a broader range of datasets. Furthermore, it makes a first step toward a statistically grounded, data-driven approach to general many-body systems with two-body interactions.

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