  Stochastic differential equations play an important role in various
applications when modeling systems that have either random perturbations or
chaotic dynamics at faster time scales. The time evolution of the probability
distribution of a stochastic differential equation is described by the
Fokker-Planck equation, which is a second order parabolic partial differential
equation. Previous work combined artificial neural network and Monte Carlo data
to solve stationary Fokker-Planck equations. This paper extends this approach
to time dependent Fokker-Planck equations. The focus is on the investigation of
algorithms for training a neural network that has multi-scale loss functions.
Additionally, a new approach for collocation point sampling is proposed. A few
1D and 2D numerical examples are demonstrated.
