  The time evolution of the probability distribution of a stochastic
differential equation follows the Fokker-Planck equation, which usually has an
unbounded, high-dimensional domain. Inspired by our early study in
\cite{li2018data}, we propose a mesh-free Fokker-Planck solver, in which the
solution to the Fokker-Planck equation is now represented by a neural network.
The presence of the differential operator in the loss function improves the
accuracy of the neural network representation and reduces the the demand of
data in the training process. Several high dimensional numerical examples are
demonstrated.
