  Stochastic evolution equations describing the dynamics of systems under the
influence of both deterministic and stochastic forces are prevalent in all
fields of science. Yet, identifying these systems from sparse-in-time
observations remains still a challenging endeavour. Existing approaches focus
either on the temporal structure of the observations by relying on conditional
expectations, discarding thereby information ingrained in the geometry of the
system's invariant density; or employ geometric approximations of the invariant
density, which are nevertheless restricted to systems with conservative forces.
Here we propose a method that reconciles these two paradigms. We introduce a
new data-driven path augmentation scheme that takes the local observation
geometry into account. By employing non-parametric inference on the augmented
paths, we can efficiently identify the deterministic driving forces of the
underlying system for systems observed at low sampling rates.
