Speaker: Yoshitaka Saiki, Hitotsubashi University, Japan
Title: 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 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 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. This is the joint work with Kengo Nakai (University of Tokyo).

Time: Friday, February 1, 2019, 1:30-2:30pm

Place: Exploratory Hall, Room 4106

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