Motoyasu Kanazawa

Motoyasu Kanazawa

Assistant Professor, Ocean Technology, Policy, and Environment, Graduate School of Frontier Sciences, The University of Tokyo

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Research

My research concerns the dynamic behavior of ships, floating bodies, and slender marine structures. I am especially interested in cooperative modeling methods that combine physics-based models with data-driven methods, including machine learning, for motion prediction and system identification.

During my Ph.D. work, I developed a framework for data-driven enhancement of ship dynamic models: using available physics-based models as a base, and improving their predictive performance with operational data. This line of work has been applied to ship docking, maneuvering motion prediction, and model transfer between similar ships.

I have also worked on analytical and measurement methods for marine structures, including simplified lumped-mass modeling of free-hanging slender structures and image-based motion analysis of flexible membrane cages for offshore aquaculture.

News

Publications

Peer-Reviewed Journal Papers

  1. M. Kanazawa, R. Wada, T. Wang, and H. Zhang, "Efficient Representation of New Ship Dynamics Through Reuse of Existing Maneuvering Models," IEEE Transactions on Automation Science and Engineering, 2026. preprint.
  2. T. Wang, R. Skulstad, M. Kanazawa, G. Li, and H. Zhang, "Learning Nonlinear Dynamics of Ocean Surface Vessel With Multistep Constraints," IEEE Transactions on Industrial Informatics, 2024.
  3. M. Kanazawa, T. Wang, Y. Ichinose, R. Skulstad, G. Li, and H. Zhang, "Bridging Similar Ships' Dynamics for Safeguarding the System Identification of Maneuvering Models," Ocean Engineering, 2023.
  4. T. Wang, R. Skulstad, M. Kanazawa, G. Li, and H. Zhang, "Knowledge Transfer Strategy for Enhancement of Ship Maneuvering Model," Ocean Engineering, 2023.
  5. M. Kanazawa, T. Wang, R. Skulstad, G. Li, and H. Zhang, "Knowledge and Data in Cooperative Modeling: Case Studies on Ship Trajectory Prediction," Ocean Engineering, 2022.
  6. M. Kanazawa, R. Skulstad, G. Li, L. I. Hatledal, and H. Zhang, "A Multiple-Output Hybrid Ship Trajectory Predictor With Consideration for Future Command Assumption," IEEE Sensors Journal, 2021.
  7. M. Kanazawa, R. Skulstad, T. Wang, G. Li, L. I. Hatledal, and H. Zhang, "A Physics-Data Co-Operative Ship Dynamic Model for a Docking Operation," IEEE Sensors Journal, 2021.
  8. M. Mukhlas, P. F. Lader, D. Kristiansen, T. Kristiansen, and M. Kanazawa, "Bag and Floater Motions of a Fabric Membrane Cage," Journal of Fluids and Structures, 2021.
  9. M. Kanazawa, R. Wada, and M. Ozaki, "Analytical Investigation of Key Factors for Dynamic Minimum Tension in Free-Hanging Slender Structures With Simplified Lumped Mass Model," Ocean Engineering, 2020.

Conference Papers and Proceedings

  1. M. Kanazawa, K. Mikami, R. Wada, and H. Murayama, "Bayesian Estimation of Voyage-Dependent Speed Performance of Cargo Ships," Proceedings of the SNAME Maritime Convention (SMC), 2026.
  2. M. Liu, X. Song, R. Wada, M. Kanazawa, Z. Zhu, M. Zhou, B. Wang, Q. Li, Y. Wang, and T. Pan, "From Reactive to Proactive: A Physics-Guided Cross-Well Knowledge Coupling Framework for Stuck-Pipe Early Warning," Proceedings of the SPE Annual Technical Conference and Exhibition (ATCE), 2026, SPE-233871-MS.
  3. M. Kanazawa, T. Wang, R. Skulstad, G. Li, and H. Zhang, "Physics-Data Cooperative Ship Motion Prediction With Onboard Wave Radar for Safe Operations," Proceedings of the 32nd International Symposium on Industrial Electronics (ISIE), 2023.
  4. M. Kanazawa, L. I. Hatledal, G. Li, and H. Zhang, "Co-Simulation-Based Pre-Training of a Ship Trajectory Predictor," Lecture Notes in Computer Science, Springer, 2022.
  5. T. Wang, L. I. Hatledal, M. Kanazawa, G. Li, and H. Zhang, "Effect of Ship Propulsion Retrofit on Maneuverability Research Based on Co-Simulation," Lecture Notes in Computer Science, Springer, 2022.
  6. T. Wang, R. Skulstad, M. Kanazawa, G. Li, V. Aesoy, and H. Zhang, "Physics-Informed Data-Driven Approach for Ship Docking Prediction," Proceedings of the IEEE International Conference on Real-Time Computing and Robotics (RCAR), 2022.
  7. T. Wang, R. Skulstad, M. Kanazawa, L. I. Hatledal, G. Li, and H. Zhang, "Adaptive Data-Driven Predictor of Ship Maneuvering Motion Under Varying Ocean Environments," Proceedings of the 11th International Symposium on Leveraging Applications of Formal Methods (ISoLA), 2022.
  8. B. Wu, P. Han, M. Kanazawa, H. P. Hildre, L. Zhao, H. Zhang, and G. Li, "A Camera-Based Deep-Learning Solution for Visual Attention Zone Recognition in Maritime Navigational Operations," Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022.
  9. M. Kanazawa, R. Wada, and M. Ozaki, "Analytical Approach on Dynamic Tension of Free Hanging Configuration," Proceedings of the ASME 37th International Conference on Ocean, Offshore and Arctic Engineering (OMAE), 2018.
  10. M. Ito, M. Kanazawa, R. Wada, and M. Ozaki, "Three-Dimensional Performance Analysis of a Floating Breakwater Craft for Offshore Operation," Conference Proceedings of the Japan Society of Naval Architects and Ocean Engineers, 2017.
  11. R. Wada, M. Kanazawa, M. Yamamoto, and H. Yoshimoto, "Phase Difference of Mooring Tension in Dynamic Analysis," Conference Proceedings of the Japan Society of Naval Architects and Ocean Engineers, 2016.

Achievements

Teaching

Education

Ph.D. thesis: Data-Driven Enhancement to Ship Dynamic Model for Motion Prediction.

Experience