Work

Dynamical system analysis of a data-driven model constructed by reservoir computing

Year: 2021

Type: article

Abstract: This study evaluates data-driven models from a dynamical system perspective, such as unstable fixed points, periodic orbits, chaotic saddle, Lyapunov exponents, manifold structures, and statistical values. We find that these dynamical characteristics can be reconstructed much more precisely by a data-driven model than by computing directly from training data. With this idea, we predict the laminar lasting time distribution of a particular macroscopic variable of chaotic fluid flow, which cannot be calculated from a direct numerical simulation of the Navier-Stokes equation because of its high computational cost.

Source: Physical review. E

DOI: 10.1103/physreve.104.044215

Authors: Miki U. Kobayashi, Kengo Nakai, Yoshitaka Saiki, Natsuki Tsutsumi

Institutions: Rissho University, Tokyo University of Marine Science and Technology, Hitotsubashi University

Language: English

Topic: Neural Networks and Reservoir Computing

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Kyoto University, Japan Society for the Promotion of Science

Awards: Intermittency due to hetero-chaotic property and its inference, Controlling diffusion processes in stochastic systems through the time-delayed feedback method, Expediting and Elaborating Business Forecast Using Machine Learning