Learning the temporal evolution of multivariate densities via normalizing flows. (arXiv:2107.13735v2 [stat.ML] UPDATED)

Web: http://arxiv.org/abs/2107.13735

In this work, we propose a method to learn multivariate probability
distributions using sample path data from stochastic differential equations.
Specifically, we consider temporally evolving probability distributions (e.g.,
those produced by integrating local or nonlocal Fokker-Planck equations). We
analyze this evolution through machine learning assisted construction of a
time-dependent mapping that takes a reference distribution (say, a Gaussian) to
each and every instance of our evolving distribution. If the reference
distribution is the initial condition of a Fokker-Planck equation, what …

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