I am an applied mathematician or mathematically inclined physicist. Based on dynamical systems theory, I am broadly interested in fluid mechanics and data science. In particular, I study Navier-Stokes turbulence and data-driven methods, including machine learning and data assimilation, for modelling and predicting chaotic dynamics.

I am an Associate Professor in the Department of Applied Mathematics at Tokyo University of Science, currently on sabbatical at the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge.

News

  • Our new paper entitled "Stable reproducibility of turbulence dynamics by machine learning" has been published in Physical Review Fluids. (Nov 2024) New!
  • I was honoured to receive the "Award for Distinguished Young Researcher in Fluid Mechanics" from the Japan Society of Fluid Mechanics. (Sep 2024) New!
  • Our new paper entitled "Characterizing small-scale dynamics of Navier-Stokes turbulence with transverse Lyapunov exponents: A data assimilation approach" has been published in Physical Review Letters. (Dec 2023) New!
  • Our new paper entitled Fluid mixing optimization with reinforcement learning has been published in Scientific Reports. Featured in EurekAlert! "Mixing things up: optimizing fluid mixing with machine learning" (Aug 2022)

    • About Me

    Fundamentals

    Name: Masanobu INUBUSHI
    Present position:

    - Associate Professor, Department of Applied Mathematics, Tokyo University of Science

    - Guest Associate Professor, Graduate School of Engineering Science, Osaka University

    - Visiting Scholar, Department of Applied Mathematics and Theoretical Physics, University of Cambridge

    Email: inubushi (at) rs.tus.ac.jp


    Academic Career

    Apr. 2021 - present position

    Mar. 2018 - Mar. 2021: Assistant Professor

    - Fluid Mechanics Group, Graduate School of Engineering Science, Osaka University

    Apr. 2013 - Feb. 2018: Researcher

    - NTT Communication Science Laboratories

    Apr. 2012 - Mar. 2013: JSPS Research Fellow (DC2)

    - Research Institute for Mathematical Sciences, Kyoto University


    Education

    Mar. 2013: PhD in Mathematics

    Research Institute for Mathematical Sciences, Kyoto University

    Supervisor: Prof. Michio Yamada and Assoc. Prof. Shin-ichi Takehiro

    Thesis title: Covariant Lyapunov Analysis of Navier-Stokes Turbulence

    Mar. 2010: MSc in Mathematics

    Research Institute for Mathematical Sciences, Kyoto University

    Mar. 2008: BEng in Mechano-Aerospace Engineering

    Department of Mechano-Aerospace Engineering, Tokyo Institute of Technology

    Research Interests

  • Nonlinear dynamics in fluid mechanics

    Keywords: instability, (covariant) Lyapunov analysis, mixing, and turbulence

  • Data science for fluid mechanics

    Keywords: reservoir computing, reinforcement learning, and data-assimilation

  • Nonlinear dynamics in complex photonics

    Keywords: reservoir computing, random number generation, and synchronization

    • Publications

    Peer-reviewed papers
    1. Satoshi Matsumoto, Masanobu Inubushi, and Susumu Goto,
      "Stable reproducibility of turbulence dynamics by machine learning",
      Physical Review Fluids 9, 104601 (2024).
      https://journals.aps.org/prfluids/abstract/10.1103/PhysRevFluids.9.104601 New!
    2. Masanobu Inubushi, Yoshitaka Saiki, Miki U. Kobayashi, and Susumu Goto,
      "Characterizing Small-Scale Dynamics of Navier-Stokes Turbulence with Transverse Lyapunov Exponents: A Data Assimilation Approach",
      Physical Review Letters 131, 254001 (2023).
      https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.131.254001 New!
    3. Yuto Iwasaki, Takayuki Nagata, Yasuo Sasaki, Kumi Nakai, Masanobu Inubushi, and Taku Nonomura,
      "Reservoir computing reduced-order model based on particle image velocimetry data of post-stall flow",
      AIP Advances 13, 065312 (2023).
      https://pubs.aip.org/aip/adv/article/13/6/065312/2894878/Reservoir-computing-reduced-order-model-based-on New!
    4. Mikito Konishi, Masanobu Inubushi, and Susumu Goto,
      "Fluid mixing optimization with reinforcement learning",
      Scientific Reports 12, 14268 (2022).
      https://www.nature.com/articles/s41598-022-18037-7 New!
      Featured in EurekAlert!
      "Mixing things up: optimizing fluid mixing with machine learning" New!
    5. Masanobu Inubushi and Susumu Goto,
      "Transfer learning for nonlinear dynamics and its application to fluid turbulence",
      Physical Review E 102, 043301 (2020).
      https://arxiv.org/abs/2009.01407
      https://journals.aps.org/pre/abstract/10.1103/PhysRevE.102.043301
    6. Takumi Yokosaka, Masanobu Inubushi, Scinob Kuroki, and Junji Watanabe,
      "Frequency of Switching Touching Mode Reflects Tactile Preference Judgment",
      Scientific Reports 10, 3022 (2020).
    7. Masanobu Inubushi,
      "Unpredictability and robustness of chaotic dynamics for physical random number generation",
      Chaos: An Interdisciplinary Journal of Nonlinear Science 29, 033133 (2019).
      https://aip.scitation.org/doi/10.1063/1.5090177
    8. Kosuke Takano, Chihiro Sugano, Masanobu Inubushi, Kazuyuki Yoshimura, Satoshi Sunada, Kazutaka Kanno, and Atsushi Uchida,
      "Compact reservoir computing with a photonic integrated circuit",
      Optics Express, 26(22) 29424-29439 (2018).
    9. Makoto Tomiyama, Kazuto Yamasaki, Kenichi Arai, Masanobu Inubushi, Kazuyuki Yoshimura, and Atsushi Uchida,
      "Effect of bandwidth limitation of optical noise injection on common-signal-induced synchronization in multi-mode semiconductor lasers",
      Optics Express, 26(10), 13521-13535 (2018).
    10. Takuma Sasaki, Izumi Kakesu, Yusuke Mitsui, Damien Rontani, Atsushi Uchida, Satoshi Sunada, Kazuyuki Yoshimura, and Masanobu Inubushi,
      “Common-signal-induced synchronization in photonic integrated circuits and its application to secure key distribution”,
      Optics Express, 25(21), 26029-26044 (2017).
    11. Shoma Ohara, Andreas Karsaklian Dal Bosco, Kazusa Ugajin, Atsushi Uchida, Takahisa Harayama, and Masanobu Inubushi,
      "Dynamics-dependent synchronization in on-chip coupled semiconductor lasers",
      Physical Review E 96, 032216 (2017).
    12. Masanobu Inubushi and Kazuyuki Yoshimura,
      "Reservoir Computing Beyond Memory-Nonlinearity Trade-off",
      Scientific Reports 7, 10199 (2017).
      https://www.nature.com/articles/s41598-017-10257-6
    13. Tomohiro Ito, Hayato Koizumi, Nobumitsu Suzuki, Izumi Kakesu, Kento Iwakawa, Atsushi Uchida, Takeshi Koshiba, Jun Muramatsu, Kazuyuki Yoshimura, Masanobu Inubushi, and Peter Davis,
      "Physical implementation of oblivious transfer using optical correlated randomness",
      Scientific Reports 7, 8444 (2017).
      https://www.nature.com/articles/s41598-017-08229-x
    14. Andreas Karsaklian Dal Bosco, Naoki Sato, Yuta Terashima, Shoma Ohara, Atsushi Uchida, Takahisa Harayama, and Masanobu Inubushi,
      "Random number generation from intermittent optical chaos",
      IEEE Journal of Selected Topics in Quantum Electronics, vol. 23, no. 6, pp. 1-8, (2017).
    15. Nobumitsu Suzuki, Takuya Hida, Makoto Tomiyama, Atsushi Uchida, Kazuyuki Yoshimura, Kenichi Arai, and Masanobu Inubushi,
      "Common-signal-induced synchronization in semiconductor lasers with broadband optical noise signal",
      IEEE Journal of Selected Topics in Quantum Electronics, vol. 23, no. 6, pp. 1-10, (2017).
    16. Andreas Karsaklian Dal Bosco, Syoma Ohara, Naoki Sato, Yasuhiro Akizawa, Atsushi Uchida, Takahisa Harayama, and Masanobu Inubushi,
      "Dynamics versus feedback delay time in photonic integrated circuits: Mapping the short cavity regime",
      IEEE Photonics Journal, Volume: 9, Issue: 2 (2017).
    17. Kazusa Ugajin, Yuta Terashima, Kento Iwakawa, Atsushi Uchida, Takahisa Harayama, Kazuyuki Yoshimura, and Masanobu Inubushi,
      "Real-time fast physical random number generator with a photonic integrated circuit",
      Optics Express 25(6), 6511-6523 (2017).
    18. Masanobu Inubushi, Kazuyuki Yoshimura, and Peter Davis
      "Noise robustness of unpredictability in a chaotic laser system: Toward reliable physical random bit generation”
      Physical Review E 91, 022918 (2015).
    19. Masanobu Inubushi, Kazuyuki Yoshimura, Kenichi Arai, and Peter Davis
      “Physical random bit generators and their reliability: focusing on chaotic laser systems”
      Nonlinear Theory and Its Applications (invited paper), IEICE, vol. 6 no. 2 (2015).
    20. Masanobu Inubushi, Shin-ichi Takehiro, Michio Yamada
      “Regeneration cycle and the covariant Lyapunov vectors in a minimal wall turbulence''
      Physical Review E 92, 023022 (2015).
    21. Masanobu Inubushi, Miki U Kobayashi, Shin-ichi Takehiro, and Michio Yamada
      “Covariant Lyapunov Analysis of Chaotic Kolmogorov Flows”
      Physical Review E 85, 016331 (2012).
      Peer-reviewed proceedings
    1. Masanobu Inubushi and Susumu Goto
      Transferring Reservoir Computing: Formulation and Application to Fluid Physics,
      Lecture Notes in Computer Science 11731, 193, Springer (2019). https://link.springer.com/chapter/10.1007/978-3-030-30493-5_22
    2. Mitsumasa Nakajima, Masanobu Inubushi, Takashi Goh, and Toshikazu Hashimoto
      Coherently Driven Ultrafast Complex-Valued Photonic Reservoir Computing, Proceedings Conference on Lasers and Electro-Optics, page SM1C.4 (2018). https://www.osapublishing.org/abstract.cfm?URI=CLEO_SI-2018-SM1C.4
    3. Masanobu Inubushi, Miki U Kobayashi, Shin-ichi Takehiro, Michio Yamada
      Covariant Lyapunov Analysis of Chaotic Kolmogorov Flows and Time-correlation Function,
      Procedia IUTAM, 5, 244-248 (2012).
      https://www.sciencedirect.com/science/article/pii/S2210983812000934
    Book chapters
    1. Masanobu Inubushi, Kazuyuki Yoshimura, Yoshiaki Ikeda, and Yuto Nagasawa,
      On the Characteristics and Structures of Dynamical Systems Suitable for Reservoir Computing,
      Chapter 5, Reservoir Computing -Theory, Physical Implementations, and Applications-, Kohei Nakajima and Ingo Fischer (Eds.), Springer (2021).New!
      [link]