DeepParticle: Learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method

Abstract

We introduce DeepParticle, a method to learn and generate invariant measures of stochastic dynamical systems with physical parameters based on data computed from an interacting particle method (IPM). We utilize the expressiveness of deep neural networks (DNNs) to represent the transform of samples from a given input (source) distribution to an arbitrary target distribution, neither assuming distribution functions in closed form nor the sample transforms to be invertible, nor the sample state space to be finite. In the training stage, we update the weights of the network by minimizing a discrete Wasserstein distance between the input and target samples. To reduce the computational cost, we propose an iterative divide-and-conquer (a mini-batch interior point) algorithm, to find the optimal transition matrix in the Wasserstein distance. We present numerical results to demonstrate the performance of our method for accelerating the computation of invariant measures of stochastic dynamical systems arising in computing reaction-diffusion front speeds in 3D chaotic flows by using the IPM. The physical parameter is a large Péclet number reflecting the advection-dominated regime of our interest.