A continuous normalizing flow can be framed as a gradient-flow optimization that transforms an initial density, such as a normal distribution, into a target density, yielding an optimal-transport-style problem.

“Of finding a density as you start off with kind of initial guess. Just as an just as an optimization you start with initial guess, which is like your normal distribution and then you end up with your your final density. And what you end up doing is you need to kind of find effectively an optimization process which gets you from there to there, which is a gradient flow which is what gives you these uh like effectively optimal transport as style problems.”