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

Kobayashi, Miki U; Nakai, Kengo; Saiki, Yoshitaka; Tsutsumi, Natsuki

Kobayashi, Miki U; Nakai, Kengo; Saiki, Yoshitaka; Tsutsumi, Natsuki.

Afiliación

Kobayashi MU; Faculty of Economics, Rissho University, Tokyo 141-8602, Japan.
Nakai K; Faculty of Marine Technology, Tokyo University of Marine Science and Technology, Tokyo 135-8533, Japan.
Saiki Y; Graduate School of Business Administration, Hitotsubashi University, Tokyo 186-8601, Japan.
Tsutsumi N; Faculty of Commerce and Management, Hitotsubashi University, Tokyo 186-8601, Japan.

Phys Rev E ; 104(4-1): 044215, 2021 Oct.

Article en En | MEDLINE | ID: mdl-34781491

RESUMEN

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.

Texto completo

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Phys Rev E Año: 2021 Tipo del documento: Article País de afiliación: Japón Pais de publicación: Estados Unidos

Texto completo

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Buscar en Google