Data assimilation for networks of coupled oscillators: Inferring unknown model parameters from partial observations

TL;DR

This paper develops a localized Ensemble Kalman Filter approach to accurately infer states and unknown parameters in networks of coupled oscillators from partial, noisy observations, outperforming standard methods.

Contribution

It introduces a network-specific localization technique within the Ensemble Kalman Filter framework for improved inference in high-dimensional oscillator networks.

Findings

Highly accurate estimation of oscillator phases and parameters achieved

Localization significantly improves inference over standard methods

Effective across different network topologies and oscillator models

Abstract

Inferring the state and unknown parameters of a network of coupled oscillators is of utmost importance. This task is made harder when only partial and noisy observations are available, which is a typical scenario in realistic high-dimensional systems. The general task of inference falls under data assimilation, and a commonly used assimilation method is the Ensemble Kalman Filter. Employing network-specific localization of the forecast covariance, an Ensemble Kalman Filter with state space augmentation is shown to yield highly accurate estimates of both the oscillator phases and unknown model parameters in the case where only a subset of oscillator phases are observed. In contrast, standard data assimilation methods yield poor results. We demonstrate the effectiveness of our approach for Kuramoto oscillators and for networks of theta neurons, using a variety of network topologies.