K. NAKAI Publications

Page of Kengo NAKAI (中井 拳吾)


Publications

■ Articles in refereed journal
  1. New! N. Tsutsumi and K. Nakai and Y. Saiki, Constructing low-dimensional ordinary differential equations from chaotic time series of high/infinite-dimensional systems using radial function-based regression, Physical Review E 108, 054220 (2023). link, arXiv link
  2. N. Tsutsumi and K. Nakai and Y. Saiki, Constructing differential equations using only a scalar time-series about continuous time chaotic dynamics, Chaos 32, 091101 (2022). link, arXiv link
  3. M. Kobayashi, K. Nakai, Y. Saiki, and N. Tsutsumi, Dynamical system analysis of a data-driven model constructed by reservoir computing, Physical Review E 104, 044215:1-7 (2021). link, arXiv link
  4. K. Nakai and Y. Saiki, Machine-learning construction of a model for a macroscopic fluid variable using the delay-coordinate of a scalar observable, Discrete and Continuous Dynamical Systems Series S 14, 3 (2021). link, arXiv link
  5. K. Nakai, Direction of Vorticity and a Refined Regularity Criterion for the Navier--Stokes Equations with Fractional Laplacian, Journal of Mathematical Fluid Mechanics 21, 21 (2019). link
  6. K. Nakai and Y. Saiki, Machine-learning inference of fluid variables from data using reservoir computing, Physical Review E 98, 023111:1-6 (2018). link, arXiv link

■ Preprints/Papers in preparation
  • New! Tamaki Suematsu, Kengo Nakai, Tsuyoshi Yoneda, Daisuke Takasuka, Takuya Jinno, Yoshitaka Saiki, and Hiroaki Miura, Machine learning prediction of the MJO extends beyond one month, ArXiv arXiv link

    ■ Others (some are in Japanese)
    ▼Click Here

    Talks (include 29 invited talks)

    ■ Talks in English
    1. (Invited)20th Prediction Science Seminar, "Implementing Reservoir Computing in Practice" RIKEN, November 24th 2023.
    2. XLIII Dynamics Days Europe, "Constructing a data-driven model of intraseasonal weather time-series using machine learning" Università degli Studi di Napoli Federico II, (Italy), September 6th 2023.
    3. 10th International Congress on Industrial and Applied Mathematics (ICIAM2023), "Evaluation of a data-driven model using reservoir computing from dynamical system point of view" Waseda University, August ***th 2023.
    4. (Invited)International Workshop on Ergodic Theory, Dynamical Systems, and Climate Sciences, "Constructing a data-driven model of intraseasonal weather time-series using machine learning" Hokkaido University, March 09th 2023.
    5. (Invited)Hirosaki University Workshop on Nonlinear Science 2022, "Dynamical system analysis of a data-driven model constructed by reservoir computing" Hirosaki University, Nov. 14th 2022.
    6. Dynamics days europe 2022, "Evaluation of a data-driven model using reservoir computing from dynamical system point of view" The University of Aberdeen, (United Kingdom), Aug. 22nd 2022.
    7. Japan Geoscience Union Meeting 2022, "Subseasonal prediction of Madden-Julian Oscillation using machine learning", TOKYO BIG SIGHT, May 25th 2022.
    8. (Invited)Differential Equations for Data Science 2022 , "Constructing differential equations using only a chaotic time-series", Online, Mar.23th 2022.
    9. (Invited)Differential Equations for Data Science 2021 , "Dynamical system analysis of a data-driven model constructed by reservoir computing" Online, Mar.10th 2021.
    10. (Invited)SOI Asia, "Machine Learning for Time Series Data" Keio University, Jan.20th 2021.
    11. (Invited)NOLTA2019, "Machine-learning inference of variables of a chaotic fluid flow from data using reservoir computing" Kuala Lumpur, (Malaysia), Dec.2019.
    12. (Invited)RIMS Seminar, "Machine-learning construction of a model for a macroscopic fluid variable by using reservoir computing" Hokkaido Niseko, Sep.7th--10th, 2019.
    13. (Invited)Recent topics on well-posedness and stability of incompressible fluid and related topics, "Machine-learning construction of a model for a macroscopic fluid variable" Berkeley, California, (U.S.), July.2019.
    14. (Invited)A3 Workshop at Kobe & Naruto, "Machine-learning construction of a model for a macroscopic fluid variable using the delay-coordinate of a scalar observable" Kobe & Naruto, Mar.2019.
    15. ICMMA 2018 -Data Science, Time Series Modeling and Applications-, "Machine-learning Inference of Fluid Variables from Data by using Reservoir Computing" Nakano Campus, Meiji University, February 11-13,　2019.
    16. Dynamics Days 2019, "Machine-learning Inference of Fluid Variables from Data by using Reservoir Computing" Evanston, IL (U.S.), January 4-6.2019(flash talk).
    17. (Invited)Analysis of Fluids and Related Topics, "Machine-learning inference of fluid variables from data using reservoir computing" Princeton University (U.S.), Sep.2018.
    18. Czech-Japanese Seminar in Applied Mathematics 2018, "Machine-learning inference of fluid variables from data using reservoir computing" Ishikawa, July 13--16 2018.
    19. EASIAM2018, "Machine-learning prediction of fluid variables from data using reservoir computation" University of Tokyo, June 22--25, 2018.
    20. (Invited)Sun Yat-sen University seminar, "Regurarity of General Navier-Stokes equations and Energy Cascade" Sun Yat-sen University (China), Nov.2017.
    21. (Invited)Princeton-Tokyo Fluid Mechanics Workshop, "Mathematical and numerical analysis of the energy cascade for the Navier-Stokes Equations with partial hyperviscosity" Princeton University (U.S.), Nov.2017.
    22. (Invited)Mathematical Analysis in Fluid and Gas Dynamics, "Direction of vorticity and a refined blow-up criterion for the Navier-Stokes equations with fractional Laplacian" RIMS (Japan), Jul.2017.

    ■ Talks in Japanese
    ▼Click Here

    ■ Poster
    ▼Click Here

    Last Modified Date: 2020.05