We consider solving a class of Fokker Planck Equations and some related problems using ideas from generative models of machine learning. In particular, we consider isometric immersions into the Wasserstein space, and use the gradient flow structure to derive an ODE in the parameter space. Other possible formulations will be mentioned as well as some recent new advances done by other groups.