| Abstract: | 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. |
