83 citations · 131 across the 10 of their papers we have counts for
11 papers
Geometric structure of ideal data-driven dynamical model using RfR method
Natsuki Tsutsumi, Kengo Nakai, Yoshitaka Saiki
The Gaussian radial function-based Regression (RfR) method is a data-driven modeling approach that utilizes physically understandable variables from scalar time series, constructed…
On the attractor in a high-dimensional neural network dynamics of reservoir computing: Lyapunov analysis viewpoint
Miki U. Kobayashi, Kengo Nakai, Yoshitaka Saiki +1
Recent theoretical developments of reservoir computing have clarified a sufficient condition about which reservoir computing can capture the dynamics of a target system, enabling t…
Long-term prediction of El Niño-Southern Oscillation using reservoir computing with data-driven realtime filter
Takuya Jinno, Takahito Mitsui, Kengo Nakai +2
In recent years, the application of machine learning approaches to time-series forecasting of climate dynamical phenomena has become increasingly active. It is known that applying…
Data-driven ODE modeling of the high-frequency complex dynamics via a low-frequency dynamics model
Natsuki Tsutsumi, Kengo Nakai, Yoshitaka Saiki
In our previous paper [N. Tsutsumi, K. Nakai and Y. Saiki, Chaos 32, 091101 (2022)], we proposed a method for constructing a system of differential equations of chaotic behavior fr…
Data-driven modeling from biased small training data using periodic orbits
Kengo Nakai, Yoshitaka Saiki
In this study, we investigate the effect of reservoir computing training data on the reconstruction of chaotic dynamics. Our findings indicate that a training time series comprisin…
Constructing low-dimensional ordinary differential equations from chaotic time series of high/infinite-dimensional systems using radial function-based regression
Natsuki Tsutsumi, Kengo Nakai, Yoshitaka Saiki
In our previous study (N. Tsutsumi, K. Nakai and Y. Saiki (2022)) we proposed a method of constructing a system of differential equations of chaotic behavior only from observable d…
Machine learning prediction of the MJO extends beyond one month
Tamaki Suematsu, Kengo Nakai, Tsuyoshi Yoneda +4
The prediction of the Madden-Julian Oscillation (MJO), a massive tropical weather event with vast global socio-economic impacts, has been infamously difficult with physics-based we…
Constructing differential equations using only a scalar time-series about continuous time chaotic dynamics
Natsuki Tsutsumi, Kengo Nakai, Yoshitaka Saiki
We propose a simple method of constructing a system of differential equations of chaotic behavior based on the regression only from a scalar observable time-series data. The estima…
Dynamical system analysis of a data-driven model constructed by reservoir computing
Miki U Kobayashi, Kengo Nakai, Yoshitaka Saiki +1
This study evaluates data-driven models from a dynamical system perspective, such as unstable fixed points, periodic orbits, chaotic saddle, Lyapunov exponents, manifold structures…
Machine-learning construction of a model for a macroscopic fluid variable using the delay-coordinate of a scalar observable
Kengo Nakai, Yoshitaka Saiki
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.…
Machine-learning inference of fluid variables from data using reservoir computing
Kengo Nakai, Yoshitaka Saiki
We infer both microscopic and macroscopic behaviors of a three-dimensional chaotic fluid flow using reservoir computing. In our procedure of the inference, we assume no prior knowl…