Efficient Numerical Approach for Solving High-Dimensional Wasserstein Gradient Flows

The authors develop a fast and scalable numerical approach to solve Wasserstein gradient flows, particularly suitable for high-dimensional cases, by parameterizing the push-forward maps using general reduced-order models like deep neural networks.