  We present a method for the nonparametric estimation of the drift function of
certain types of stochastic differential equations from the empirical density.
It is based on a variational formulation of the Fokker-Planck equation. The
minimization of an empirical estimate of the variational functional using
kernel based regularization can be performed in closed form. We demonstrate the
performance of the method on second order, Langevin-type equations and show how
the method can be generalized to other noise models.

Data Analysis, Statistics and Probability