  We develop a data-driven method combining a forecast model with unknown
parameters and partial noisy observations for networks of coupled oscillators.
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.
