Generative Adversarial Network for Probabilistic Forecast of Random Dynamical System
Abstract
We present a deep learning model for data-driven simulations of random dynamical systems without a distributional assumption. The deep learning model consists of a recurrent neural network, which aims to learn the time marching structure, and a generative adversarial network (GAN) to learn and sample from the probability distribution of the random dynamical system. Although GANs provide a powerful tool to model a complex probability distribution, the training often fails without a proper regularization. Here, we propose a regularization strategy for a GAN based on consistency conditions for the sequential inference problems. First, the maximum mean discrepancy (MMD) is used to enforce the consistency between conditional and marginal distributions of a stochastic process. Then, the marginal distributions of the multiple-step predictions are regularized by using MMD or from multiple discriminators. The behavior of the proposed model is studied by using three stochastic processes with complex noise structures.
Cite
@article{arxiv.2111.03126,
title = {Generative Adversarial Network for Probabilistic Forecast of Random Dynamical System},
author = {Kyongmin Yeo and Zan Li and Wesley M. Gifford},
journal= {arXiv preprint arXiv:2111.03126},
year = {2022}
}