Machine-learning construction of a model for a macroscopic fluid variable using the delay-coordinate of a scalar observable

Abstract

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.

14 pages, 7 figures

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Cited by in corpus (2)

• Dynamical system analysis of a data-driven model constructed by reservoir computing
• Constructing differential equations using only a scalar time-series about continuous time chaotic dynamics