Pretty cool in fact all the methods they demo there show good approximation of difficult distributions (if that looks easy, take a look at scikit learn’s manifold doc page). The shoe that hasn’t dropped in the article is behavior in high dimensions. For instance, I seem to recall that backwards integration of high dimensional DEs is unstable (not to mention memory issues).