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
We construct a data-driven dynamical system model for a macroscopic variable
of a high-dimensionally chaotic fluid flow by training its scalar
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
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
Department of Mathematical Sciences
George Mason University
4400 University Drive, MS 3F2
Fairfax, VA 22030-4444
Tel. 703-993-1460, Fax. 703-993-1491