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Machine-learning Construction of a Model for a Macroscopic Fluid Variable Using the Delay-coordinate of a Scalar Observable by Nakai, Kengo; Saiki, Yoshitaka is a scholarly article available to read on EtoBox.

What is Machine-learning Construction of a Model for a Macroscopic Fluid Variable Using the Delay-coordinate of a Scalar Observable about?

We construct a data-driven dynamical system model for a macroscopic variable the Reynolds number of a high-dimensionally chaotic fluid flow by training its scalar time-series data. We use a machine-learning approach, the reservoir computing for the construction of the model, and do not use the knowledge of a physical process of fluid dynamics in its procedure. It is confirmed that an inferred time-series obtained from the model approximates the actual one in each of various time-intervals, and that some characteristics of the chaotic invariant set mimic the actual ones. We investigate the appropriate choice of the delay-coordinate, especially the delay-time and the dimension, which enables us to construct a model having a relatively high-dimensional attractor easily.

Author

Nakai, Kengo; Saiki, Yoshitaka

Published

2019

Language

EN