Generative Adversarial Network for Probabilistic Forecast of Random Dynamical System

• URL: http://arxiv.org/abs/2111.03126v1
• Date: Thu, 4 Nov 2021 19:50:56 GMT
• Title: Generative Adversarial Network for Probabilistic Forecast of Random Dynamical System
• Authors: Kyongmin Yeo, Zan Li, Wesley M. Gifford
• Abstract summary: We present a deep learning model for data-driven simulations of random dynamical systems without a distributional assumption. We propose a regularization strategy for a generative adversarial network based on consistency conditions for the sequential inference problems. The behavior of the proposed model is studied by using three processes with complex noise structures.
• Score: 19.742888499307178
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 to learn and sample from the probability distribution of the random dynamical system. Although generative adversarial networks 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 generative adversarial network 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.

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