Phase coupling estimation from multivariate phase statistics

TL;DR

This paper introduces a maximum entropy-based method for estimating phase coupling parameters in multivariate oscillator systems from observed phase data, effectively solving the inverse problem.

Contribution

It derives a closed-form solution for inverse phase coupling estimation from nonlinear Langevin equations, applicable to high-dimensional and limited data scenarios.

Findings

Performs well in high-dimensional systems (d=100)

Effective with limited data (as few as 100 samples per dimension)

Provides a broadly applicable maximum entropy distribution for phase relationships

Abstract

Coupled oscillators are prevalent throughout the physical world. Dynamical system formulations of weakly coupled oscillator systems have proven effective at capturing the properties of real-world systems. However, these formulations usually deal with the `forward problem': simulating a system from known coupling parameters. Here we provide a solution to the `inverse problem': determining the coupling parameters from measurements. Starting from the dynamic equations of a system of coupled phase oscillators, given by a nonlinear Langevin equation, we derive the corresponding equilibrium distribution. This formulation leads us to the maximum entropy distribution that captures pair-wise phase relationships. To solve the inverse problem for this distribution, we derive a closed form solution for estimating the phase coupling parameters from observed phase statistics. Through simulations, we show that the algorithm performs well in high dimensions (d=100) and in cases with limited data (as few as 100 samples per dimension). Because the distribution serves as the unique maximum entropy solution for pairwise phase statistics, the distribution and estimation technique can be broadly applied to phase coupling estimation in any system of phase oscillators.