42 citations · 44 across the 3 of their papers we have counts for
5 papers
Synchronisation in two-dimensional damped-driven Navier-Stokes turbulence: insights from data assimilation and Lyapunov analysis
Masanobu Inubushi, Colm-cille P. Caulfield
In Navier--Stokes (NS) turbulence, large-scale turbulent flows inevitably determine small-scale flows. Previous studies using data assimilation with the three-dimensional NS equati…
Data-driven prediction of reversal of large-scale circulation in turbulent convection
Daigaku Katsumi, Masanobu Inubushi, Naoto Yokoyama
Large-scale circulation (LSC) quasi-stably emerges in the turbulent Rayleigh-Bénard convection, and intermittently reverses its rotational direction in two-dimensional turbulent co…
Reservoir Computing with Generalized Readout based on Generalized Synchronization
Akane Ookubo, Masanobu Inubushi
Reservoir computing is a machine learning framework that exploits nonlinear dynamics, exhibiting significant computational capabilities. One of the defining characteristics of rese…
Characterizing Data Assimilation in Navier-Stokes Turbulence with Transverse Lyapunov Exponents
Masanobu Inubushi, Yoshitaka Saiki, Miki U. Kobayashi +1
Data assimilation (DA) reconstructing small-scale turbulent structures is crucial for forecasting and understanding turbulence. This study proposes a theoretical framework for DA b…
Transfer learning for nonlinear dynamics and its application to fluid turbulence
Masanobu Inubushi, Susumu Goto
We introduce transfer learning for nonlinear dynamics, which enables efficient predictions of chaotic dynamics by utilizing a small amount of data. For the Lorenz chaos, by optimiz…