World's Best Scientists 2026 revealed!
Koji Fukagata

Koji Fukagata

D-Index & Metrics

Mechanical and Aerospace Engineering

D-Index
36
Citations
6959
World Ranking
2479
National Ranking
58

Koji Fukagata publication distribution in Mechanical and Aerospace Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mechanical and Aerospace Engineering in 2026. The highlighted bar marks where Koji Fukagata sits on this spectrum.

47–56 publications: 10 scientists 57–66 publications: 23 scientists 67–76 publications: 32 scientists 77–86 publications: 62 scientists 87–96 publications: 67 scientists 97–106 publications: 91 scientists 107–116 publications: 113 scientists 117–126 publications: 115 scientists 127–136 publications: 130 scientists 137–146 publications: 140 scientists 147–156 publications: 155 scientists 157–166 publications: 132 scientists 167–176 publications: 133 scientists 177–186 publications: 130 scientists 187–196 publications: 140 scientists 197–206 publications: 115 scientists 207–216 publications: 125 scientists 217–226 publications: 117 scientists 227–236 publications: 99 scientists 237–246 publications: 92 scientists 247–256 publications: 100 scientists 257–266 publications: 95 scientists 267–276 publications: 88 scientists 277–286 publications: 77 scientists 287–296 publications: 74 scientists 297–306 publications: 74 scientists 307–316 publications: 62 scientists 317–326 publications: 70 scientists 327–336 publications: 59 scientists 337–346 publications: 58 scientists 347–356 publications: 45 scientists 357–366 publications: 44 scientists 367–376 publications: 36 scientists 377–386 publications: 41 scientists 387–396 publications: 32 scientists 397–406 publications: 23 scientists 407–416 publications: 28 scientists 417–426 publications: 27 scientists 427–436 publications: 25 scientists 437–446 publications: 23 scientists 447–456 publications: 23 scientists 457–466 publications: 20 scientists 467–476 publications: 12 scientists 477–486 publications: 24 scientists 487–496 publications: 18 scientists 497–506 publications: 12 scientists 507–516 publications: 13 scientists 517–526 publications: 21 scientists 527–536 publications: 12 scientists 537–546 publications: 8 scientists 547–556 publications: 16 scientists 557–566 publications: 3 scientists 567–576 publications: 11 scientists 577–586 publications: 6 scientists 587–596 publications: 5 scientists 597–606 publications: 6 scientists 607–616 publications: 7 scientists 617–626 publications: 7 scientists 627–636 publications: 10 scientists 637–646 publications: 4 scientists 647–656 publications: 3 scientists 657–658 publications: 2 scientists 659+ publications: 100 scientists
47 publications 659+

This scientist: 290 publications — 70th percentile

70% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 659 publications or more.

Koji Fukagata D-index placement in Mechanical and Aerospace Engineering in 2026

The chart shows the D-index (discipline H-index) distribution of Mechanical and Aerospace Engineering scientists ranked by Research.com in 2026. The highlighted bar marks where Koji Fukagata sits on this spectrum.

30 D-Index: 83 scientists 31 D-Index: 113 scientists 32 D-Index: 144 scientists 33 D-Index: 153 scientists 34 D-Index: 189 scientists 35 D-Index: 158 scientists 36 D-Index: 139 scientists 37 D-Index: 127 scientists 38 D-Index: 130 scientists 39 D-Index: 126 scientists 40 D-Index: 104 scientists 41 D-Index: 100 scientists 42 D-Index: 107 scientists 43 D-Index: 101 scientists 44 D-Index: 103 scientists 45 D-Index: 79 scientists 46 D-Index: 88 scientists 47 D-Index: 70 scientists 48 D-Index: 83 scientists 49 D-Index: 44 scientists 50 D-Index: 64 scientists 51 D-Index: 56 scientists 52 D-Index: 50 scientists 53 D-Index: 48 scientists 54 D-Index: 58 scientists 55 D-Index: 52 scientists 56 D-Index: 48 scientists 57 D-Index: 42 scientists 58 D-Index: 34 scientists 59 D-Index: 42 scientists 60 D-Index: 37 scientists 61 D-Index: 42 scientists 62 D-Index: 44 scientists 63 D-Index: 22 scientists 64 D-Index: 33 scientists 65 D-Index: 29 scientists 66 D-Index: 23 scientists 67 D-Index: 29 scientists 68 D-Index: 24 scientists 69 D-Index: 19 scientists 70 D-Index: 34 scientists 71 D-Index: 26 scientists 72 D-Index: 19 scientists 73 D-Index: 18 scientists 74 D-Index: 19 scientists 75 D-Index: 14 scientists 76 D-Index: 19 scientists 77 D-Index: 8 scientists 78 D-Index: 18 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 17 scientists 82 D-Index: 11 scientists 83 D-Index: 16 scientists 84 D-Index: 7 scientists 85 D-Index: 9 scientists 86 D-Index: 8 scientists 87 D-Index: 6 scientists 88 D-Index: 6 scientists 89 D-Index: 7 scientists 90 D-Index: 10 scientists 91 D-Index: 4 scientists 92 D-Index: 4 scientists 93+ D-Index: 100 scientists
30 D-Index 93+

This scientist: 36 D-Index — 28th percentile

28% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 93 D-Index or more.

Overview

Koji Fukagata is affiliated with Keio University in Japan and has an extensive publication record primarily in the fields of Engineering and Physics and Astronomy. Their research contributions focus significantly on computational mechanics, statistical and nonlinear physics, aerospace engineering, artificial intelligence, and mechanical engineering.

The scientist's principal research areas encompass fluid dynamics and turbulent flows, model reduction and neural networks, fluid dynamics and vibration analysis, aerodynamics and acoustics in jet flows, heat transfer mechanisms, meteorological phenomena and simulations, and lattice Boltzmann simulation studies.

Frequent publication venues for Koji Fukagata include:

  • arXiv (Cornell University)
  • Journal of Fluid Science and Technology
  • The Proceedings of Mechanical Engineering Congress Japan
  • Physics of Fluids
  • International Journal of Heat and Fluid Flow

Their recent research papers cover various aspects of machine learning applications to fluid dynamics, including the assessment and development of neural network models for fluid flow data interpretation and surrogate modeling. Notable papers include:

  • Assessment of supervised machine learning methods for fluid flows, 2020, Theoretical and Computational Fluid Dynamics
  • Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow, 2021, Physics of Fluids
  • Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data, 2020, Physics of Fluids
  • Probabilistic neural networks for fluid flow surrogate modeling and data recovery, 2020, Physical Review Fluids
  • Super-resolution analysis via machine learning: a survey for fluid flows, 2023, Theoretical and Computational Fluid Dynamics

Their research collaborations often involve co-authors such as Kai Fukami, Taichi Nakamura, Yusuke Nabae, Masaki Morimoto, and Kunihiko Taira. These partnerships have resulted in multiple joint publications over the years, indicating a continued engagement in interdisciplinary projects.

Best Publications

  • Contribution of Reynolds stress distribution to the skin friction in wall-bounded flows

    Koji Fukagata;Kaoru Iwamoto;Nobuhide Kasagi

  • Super-resolution reconstruction of turbulent flows with machine learning

    Kai Fukami;Kai Fukami;Koji Fukagata;Kunihiko Taira;Kunihiko Taira

  • A theoretical prediction of friction drag reduction in turbulent flow by superhydrophobic surfaces

    Koji Fukagata;Nobuhide Kasagi;Petros Koumoutsakos

  • Nonlinear mode decomposition with convolutional neural networks for fluid dynamics

    Takaaki Murata;Kai Fukami;Koji Fukagata

  • Highly energy-conservative finite difference method for the cylindrical coordinate system

    Koji Fukagata;Nobuhide Kasagi

  • Assessment of supervised machine learning methods for fluid flows

    Kai Fukami;Koji Fukagata;Kunihiko Taira

  • Microelectromechanical Systems–Based Feedback Control of Turbulence for Skin Friction Reduction

    Nobuhide Kasagi;Yuji Suzuki;Koji Fukagata

  • Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows

    Kai Fukami;Koji Fukagata;Kunihiko Taira

  • Direct numerical simulation of spatially developing turbulent boundary layers with uniform blowing or suction

    Yukinori Kametani;Koji Fukagata

  • Super-resolution reconstruction of turbulent flows with machine learning

    Kai Fukami;Kai Fukami;Koji Fukagata;Kunihiko Taira;Kunihiko Taira

  • Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow

    Taichi Nakamura;Kai Fukami;Kazuto Hasegawa;Yusuke Nabae

  • Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data

    Kai Fukami;Taichi Nakamura;Koji Fukagata

  • Numerical simulation of gas–liquid two-phase flow and convective heat transfer in a micro tube

    Koji Fukagata;Nobuhide Kasagi;Poychat Ua-arayaporn;Takehiro Himeno

  • Effect of uniform blowing/suction in a turbulent boundary layer at moderate Reynolds number

    Yukinori Kametani;Koji Fukagata;Ramis Örlü;Philipp Schlatter

  • Synthetic turbulent inflow generator using machine learning

    Kai Fukami;Yusuke Nabae;Ken Kawai;Koji Fukagata

  • CNN-LSTM based reduced order modeling of two-dimensional unsteady flows around a circular cylinder at different Reynolds numbers

    Kazuto Hasegawa;Kazuto Hasegawa;Kai Fukami;Takaaki Murata;Koji Fukagata

  • Numerical simulation of flow around a circular cylinder having porous surface

    Hiroshi Naito;Koji Fukagata

  • Super-resolution analysis via machine learning: a survey for fluid flows

    Unknown

  • Pumping or drag reduction

    Jérôme Hœpffner;Koji Fukagata

  • Friction drag reduction achievable by near-wall turbulence manipulation at high Reynolds numbers

    Kaoru Iwamoto;Koji Fukagata;Nobuhide Kasagi;Yuji Suzuki

  • Relaminarization of turbulent channel flow using traveling wave-like wall deformation

    Rio Nakanishi;Hiroya Mamori;Koji Fukagata

  • Convolutional neural networks for fluid flow analysis: toward effective metamodeling and low-dimensionalization

    Masaki Morimoto;Kai Fukami;Kai Fukami;Kai Zhang;Aditya G. Nair

  • Probabilistic neural networks for fluid flow surrogate modeling and data recovery

    Romit Maulik;Kai Fukami;Nesar Ramachandra;Koji Fukagata

  • Convolutional neural networks for fluid flow analysis: toward effective metamodeling and low dimensionalization

    Masaki Morimoto;Kai Fukami;Kai Fukami;Kai Zhang;Aditya G. Nair

  • On the lower bound of net driving power in controlled duct flows

    Koji Fukagata;Kazuyasu Sugiyama;Nobuhide Kasagi

  • Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow

    Taichi Nakamura;Kai Fukami;Kazuto Hasegawa;Yusuke Nabae

  • Pumping or drag reduction

    Jérôme Hoepffner;Koji Fukagata

Frequent Co-Authors

Nobuhide Kasagi
Nobuhide Kasagi University of Tokyo
Kunihiko Taira
Kunihiko Taira University of California, Los Angeles
Philipp Schlatter
Philipp Schlatter Royal Institute of Technology
Petros Koumoutsakos
Petros Koumoutsakos Harvard University
Ramis Örlü
Ramis Örlü OsloMet – Oslo Metropolitan University
Ricardo Vinuesa
Ricardo Vinuesa University of Michigan–Ann Arbor
Yuji Suzuki
Yuji Suzuki University of Tokyo
P. Henrik Alfredsson
P. Henrik Alfredsson Royal Institute of Technology
François Gallaire
François Gallaire École Polytechnique Fédérale de Lausanne

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