  In this work, we propose an adaptive learning approach based on temporal
normalizing flows for solving time-dependent Fokker-Planck (TFP) equations. It
is well known that solutions of such equations are probability density
functions, and thus our approach relies on modelling the target solutions with
the temporal normalizing flows. The temporal normalizing flow is then trained
based on the TFP loss function, without requiring any labeled data. Being a
machine learning scheme, the proposed approach is mesh-free and can be easily
applied to high dimensional problems. We present a variety of test problems to
show the effectiveness of the learning approach.
