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Fukami Lab
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A lab that analyzes unsteady flows using data science and machine learning.
AboutAbout the lab
Reading unsteady flows through data
The Fukami Lab uses data science and machine learning to analyze flows that change from moment to moment.
The lab combines these with complex network theory, information theory, and computational fluid dynamics simulations.
The flows studied are those that appear around small aircraft, passenger aircraft, passenger cars, and industrial fluid machinery.
The lab aims to build an academic and technological foundation for understanding and controlling such flows in real time.
Key terms
- Unsteady flow
- A flow whose speed and direction keep changing over time
- Super-resolution
- A technique that reconstructs fine-resolution data from coarse-resolution data
- Manifold
- A lower-dimensional surface or curved space on which high-dimensional data actually lie
NewsLab news
Associate Professor Fukami receives the IOP Trusted Reviewer Award from the Institute of Physics (UK)
Associate Professor Fukami receives the Physical Review Fluids Reviewer Excellence 2026 Award from the American Physical Society
A student receives the Best Presentation Award at the Symposium on Aerospace Numerical Simulation Technology
An undergraduate student receives the Hatakeyama Award from the Japan Society of Mechanical Engineers
Associate Professor Fukami is selected as a Distinguished Researcher at Tohoku University
ResearchResearch themes
The lab investigates a variety of unsteady flows using data science, nonlinear machine learning, complex network theory, information theory, and computational fluid dynamics simulations. Its goal is to build a foundation for analyzing, modeling, and controlling, in real time, the flows that appear around small aircraft, passenger aircraft, passenger cars, and industrial fluid machinery.
*Illustrative imageSuper-resolution by machine learning
This research applies super-resolution, which reconstructs a fine-resolution flow field from coarse-resolution data, to unsteady flows. Because a coarse image can be regarded as a collection of values from sparsely placed sensors, the lab treats super-resolution as a method for reconstructing the entire flow field.
*Illustrative imageDiscovering manifolds from data
Even unsteady flows that look complex often lie on a low-dimensional manifold that captures the essence of their dynamics. The lab has proposed several methods for finding such manifolds from data, aiming to understand flows better and to develop better, lower-cost control methods.
*Illustrative imageDynamical models by machine learning
Unsteady flows are high-dimensional and nonlinear, so simplified models are often useful for understanding and control. The lab uses data science methods to build low-dimensional models that correctly capture both the changes in the shape of the flow and its motion.
*Illustrative imageRepresentation by phase and amplitude
If a complex flow can be expressed in easy-to-understand "coordinates," it may become possible to understand and control it with tools from applied mathematics. In particular, the lab studies methods that represent rapidly changing flows in low dimensions using phase and amplitude, combined with data-driven techniques.
MembersFaculty
The members of the Fukami Lab are faculty, lab administrative staff, postdoctoral researchers, and graduate and undergraduate students working as research assistants.As of 2026
- Associate ProfessorKai Fukami
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “CFD Orbiter Model - GPN” NASA / Public domain (cropped)
- Super-resolution by machine learning: “Jet engine blades simulation” Dale Van Zante and Jay Horowitz / Public domain (cropped)
- Discovering manifolds from data: “Lorenz system r28 s10 b2-6666” Computed in Fractint by Wikimol / Public domain (cropped)
- Dynamical models by machine learning: “Turbulent Flow in a Cyclone Separator” SimScale GmbH / CC BY-SA 4.0 (cropped)
- Representation by phase and amplitude: “VortexStreet01” Gary Koopman / Public domain (cropped)
- Related lab (齋藤(勇) 研究室): “Space Shuttle Main Engine (SSME) Test Firing - GPN” NASA / Public domain (cropped)
- Related lab (焼野 研究室): “JAXA 1.27m Hypersonic Wind Tunnel P4223597” Kestrel / CC BY-SA 4.0 (cropped)
- Related lab (槙原・原/大塚 研究室): “S121E07764 - STS-121 - View of the P6 Truss and Solar Array taken from the orbiter after undocking from the ISS during STS-121 - DPLA - 4dce24f38ba8ea0a052ec9b52a371ef4” National Aeronautics and Space Administration. Lyndon B. Johnson Space Center. 2/17/1973 / Public domain (cropped)
- Related lab (桒原 研究室): “CubeSats launched by ISS Expedition 33” NASA / Public domain (cropped)
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