5 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…