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Koji Fukagata

Koji Fukagata

Keio University
Japan

D-Index & Metrics

Discipline name D-Index World Ranking Current World Ranking National Ranking Current National Ranking Publications Citations
Mechanical and Aerospace Engineering 36 2479 2320 58 53 290 6959

Koji Fukagata publications per year

The chart shows the history of publications by Koji Fukagata between 1997 and 2025, highlighting the no. of papers published in each year and offering an overview of the publication velocity of this scholar. Koji Fukagata published across 29 years, from 1997 to 2025, averaging 10.5 papers a year. Output peaked at 24 publications in 2011. 11 of the 304 publications appeared in the last two years.

No. of publications
5 10 15 20
Bar chart. Horizontal axis: year, 1997 to 2025. Vertical axis: number of publications, 0 to 24. Peak 24 publications in 2011. 1997: 1 publication 1998: 1 publication 1999: 2 publications 2000: 2 publications 2001: 2 publications 2002: 3 publications 2003: 3 publications 2004: 6 publications 2005: 8 publications 2006: 5 publications 2007: 3 publications 2008: 8 publications 2009: 18 publications 2010: 12 publications 2011: 24 publications 2012: 13 publications 2013: 15 publications 2014: 17 publications 2015: 16 publications 2016: 14 publications 2017: 22 publications 2018: 17 publications 2019: 20 publications 2020: 21 publications 2021: 24 publications 2022: 7 publications 2023: 9 publications 2024: 6 publications 2025: 5 publications
1997 2025

304 publications in total across all disciplines

View publications per year as a table
Koji Fukagata: publications per year, 1997 to 2025
Year Publications
1997 1
1998 1
1999 2
2000 2
2001 2
2002 3
2003 3
2004 6
2005 8
2006 5
2007 3
2008 8
2009 18
2010 12
2011 24
2012 13
2013 15
2014 17
2015 16
2016 14
2017 22
2018 17
2019 20
2020 21
2021 24
2022 7
2023 9
2024 6
2025 5
Total 304
Download as CSV

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.

No. of scientists
50 100 150
Bar chart with 63 bars. Horizontal axis: publications, 47–56 to 659+. Vertical axis: number of scientists, 0 to 155. Most scientists, 155, have 147–156 publications. The last bar groups every scientist with 659 publications or more. The highlighted bar, 287–296 publications, is where this scientist sits. 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
47–56 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.

View publications distribution as a table
Number of Mechanical and Aerospace Engineering scientists by publication count, Research.com 2026 ranking edition. Based on 3,445 ranked scientists.
Publications Scientists This scientist
47–56 10
57–66 23
67–76 32
77–86 62
87–96 67
97–106 91
107–116 113
117–126 115
127–136 130
137–146 140
147–156 155
157–166 132
167–176 133
177–186 130
187–196 140
197–206 115
207–216 125
217–226 117
227–236 99
237–246 92
247–256 100
257–266 95
267–276 88
277–286 77
287–296 74 290
297–306 74
307–316 62
317–326 70
327–336 59
337–346 58
347–356 45
357–366 44
367–376 36
377–386 41
387–396 32
397–406 23
407–416 28
417–426 27
427–436 25
437–446 23
447–456 23
457–466 20
467–476 12
477–486 24
487–496 18
497–506 12
507–516 13
517–526 21
527–536 12
537–546 8
547–556 16
557–566 3
567–576 11
577–586 6
587–596 5
597–606 6
607–616 7
617–626 7
627–636 10
637–646 4
647–656 3
657–658 2
659+ 100
Download as CSV

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.

No. of scientists
50 100 150
Bar chart with 64 bars. Horizontal axis: D-Index, 30 to 93+. Vertical axis: number of scientists, 0 to 189. Most scientists, 189, have 34 D-Index. The last bar groups every scientist with 93 D-Index or more. The highlighted bar, 36 D-Index, is where this scientist sits. 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.

View D-Index distribution as a table
Number of Mechanical and Aerospace Engineering scientists by D-index, Research.com 2026 ranking edition. Based on 3,445 ranked scientists.
D-Index Scientists This scientist
30 83
31 113
32 144
33 153
34 189
35 158
36 139 36
37 127
38 130
39 126
40 104
41 100
42 107
43 101
44 103
45 79
46 88
47 70
48 83
49 44
50 64
51 56
52 50
53 48
54 58
55 52
56 48
57 42
58 34
59 42
60 37
61 42
62 44
63 22
64 33
65 29
66 23
67 29
68 24
69 19
70 34
71 26
72 19
73 18
74 19
75 14
76 19
77 8
78 18
79 16
80 12
81 17
82 11
83 16
84 7
85 9
86 8
87 6
88 6
89 7
90 10
91 4
92 4
93+ 100
Download as CSV

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
Jochen Fröhlich
Jochen Fröhlich TU Dresden

External Links

Personal website of Koji Fukagata

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Best Scientists Citing Koji Fukagata

Philipp Schlatter

Philipp Schlatter

Royal Institute of Technology

Publications: 31

Ricardo Vinuesa

Ricardo Vinuesa

University of Michigan–Ann Arbor

Publications: 28

Wolfgang Schröder

Wolfgang Schröder

RWTH Aachen University

Publications: 24

Nobuhide Kasagi

Nobuhide Kasagi

University of Tokyo

Publications: 21

Bernd R. Noack

Bernd R. Noack

Harbin Institute of Technology

Publications: 21

J. Nathan Kutz

J. Nathan Kutz

University of Washington

Publications: 15

Beverley McKeon

Beverley McKeon

California Institute of Technology

Publications: 14

Pierre Sagaut

Pierre Sagaut

Aix-Marseille University

Publications: 13

Kunihiko Taira

Kunihiko Taira

University of California, Los Angeles

Publications: 11

Shiyi Chen

Shiyi Chen

Southern University of Science and Technology

Publications: 11

Sébastien Deck

Sébastien Deck

Office National d'Études et de Recherches Aérospatiales

Publications: 11

Fazle Hussain

Fazle Hussain

Texas Tech University

Publications: 10

Wei-Xi Huang

Wei-Xi Huang

Tsinghua University

Publications: 10

John Kim

John Kim

University of California, Los Angeles

Publications: 10

Nicholas Hutchins

Nicholas Hutchins

University of Melbourne

Publications: 10

Tamer A. Zaki

Tamer A. Zaki

Johns Hopkins University

Publications: 9

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