  Given nonstationary data from molecular dynamics simulations, a Markovian
Langevin model is constructed that aims to reproduce the time evolution of the
underlying process. While at equilibrium the free energy landscape is sampled,
nonequilibrium processes can be associated with a biased energy landscape,
which accounts for finite sampling effects and external driving. Extending the
data-driven Langevin equation (dLE) approach [Phys.\ Rev.\ Lett.\ {\bf 115},
050602 (2015)] to the modeling of nonequilibrium processes, an efficient way to
calculate multidimensional Langevin fields is outlined. The dLE is shown to
correctly account for various nonequilibrium processes, including the enforced
dissociation of sodium chloride in water, the pressure-jump induced nucleation
of a liquid of hard spheres, and the conformational dynamics of a helical
peptide sampled from nonstationary short trajectories.
