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

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.

Automated summary

This study evaluates data-driven models from a dynamical system perspective, and finds that these models can more accurately reconstruct dynamical characteristics than directly computing from training data. Using this approach, the study successfully predicts the laminar lasting time distribution of a specific macroscopic variable in chaotic fluid flow.