Koji Fukagata

6.2k total citations · 6 hit papers
155 papers, 4.4k citations indexed

About

Koji Fukagata is a scholar working on Computational Mechanics, Aerospace Engineering and Mechanical Engineering. According to data from OpenAlex, Koji Fukagata has authored 155 papers receiving a total of 4.4k indexed citations (citations by other indexed papers that have themselves been cited), including 120 papers in Computational Mechanics, 57 papers in Aerospace Engineering and 38 papers in Mechanical Engineering. Recurrent topics in Koji Fukagata's work include Fluid Dynamics and Turbulent Flows (107 papers), Fluid Dynamics and Vibration Analysis (45 papers) and Heat Transfer Mechanisms (33 papers). Koji Fukagata is often cited by papers focused on Fluid Dynamics and Turbulent Flows (107 papers), Fluid Dynamics and Vibration Analysis (45 papers) and Heat Transfer Mechanisms (33 papers). Koji Fukagata collaborates with scholars based in Japan, United States and Sweden. Koji Fukagata's co-authors include Kai Fukami, Nobuhide Kasagi, Kunihiko Taira, Kaoru Iwamoto, Taichi Nakamura, Takaaki Murata, Petros Koumoutsakos, Kazuto Hasegawa, H. Naito and Hiroya Mamori and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Applied Physics and Journal of Fluid Mechanics.

In The Last Decade

Koji Fukagata

143 papers receiving 4.3k citations

Hit Papers

Contribution of Reynolds stress distribution to the skin ... 2002 2026 2010 2018 2002 2019 2019 2020 2021 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Contribution of Reynolds stress distribution to the skin friction in wall-bounded flows
    2002 492 citations Koji Fukagata, Nobuhide Kasagi et al. Physics of Fluids profile →
  2. Super-resolution reconstruction of turbulent flows with machine learning
    2019 483 citations Kai Fukami, Koji Fukagata et al. Journal of Fluid Mechanics profile →
  3. Nonlinear mode decomposition with convolutional neural networks for fluid dynamics
    2019 222 citations Kai Fukami, Koji Fukagata et al. Journal of Fluid Mechanics profile →
  4. Assessment of supervised machine learning methods for fluid flows
    2020 175 citations Kai Fukami, Koji Fukagata et al. profile →
  5. Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow
    2021 144 citations Taichi Nakamura, Kai Fukami et al. Physics of Fluids profile →
  6. Super-resolution analysis via machine learning: a survey for fluid flows
    2023 94 citations Kai Fukami, Koji Fukagata et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Koji Fukagata Japan 31 3.5k 1.4k 1.2k 967 533 155 4.4k
Heng Xiao United States 29 2.5k 0.7× 760 0.6× 1.4k 1.2× 460 0.5× 667 1.3× 85 3.8k
Kunihiko Taira United States 29 4.2k 1.2× 2.4k 1.7× 1.8k 1.6× 375 0.4× 485 0.9× 138 5.4k
Stanislav Gordeyev United States 27 3.3k 0.9× 1.7k 1.2× 607 0.5× 266 0.3× 544 1.0× 197 4.2k
Tamer A. Zaki United States 34 2.9k 0.8× 1.1k 0.8× 483 0.4× 682 0.7× 602 1.1× 132 3.5k
Thomas Bewley United States 27 2.0k 0.6× 691 0.5× 721 0.6× 380 0.4× 235 0.4× 120 2.9k
Julio Soria Australia 47 6.4k 1.8× 3.8k 2.8× 430 0.4× 986 1.0× 1.4k 2.6× 327 7.6k
Christopher J. Roy United States 27 2.8k 0.8× 1.5k 1.1× 375 0.3× 344 0.4× 647 1.2× 183 4.6k
Karthik Duraisamy United States 25 2.5k 0.7× 1.2k 0.9× 1.7k 1.4× 322 0.3× 547 1.0× 108 3.7k
Shengze Cai China 17 1.4k 0.4× 647 0.5× 2.1k 1.8× 478 0.5× 151 0.3× 44 3.5k
Julia Ling United States 23 1.7k 0.5× 911 0.7× 1.2k 1.0× 649 0.7× 289 0.5× 60 2.9k

Countries citing papers authored by Koji Fukagata

Since Specialization
Citations

This map shows the geographic impact of Koji Fukagata's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Koji Fukagata with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Koji Fukagata more than expected).

Fields of papers citing papers by Koji Fukagata

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Koji Fukagata. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Koji Fukagata. The network helps show where Koji Fukagata may publish in the future.

Co-authorship network of co-authors of Koji Fukagata

This figure shows the co-authorship network connecting the top 25 collaborators of Koji Fukagata. A scholar is included among the top collaborators of Koji Fukagata based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Koji Fukagata. Koji Fukagata is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Fukagata, Koji & Kai Fukami. (2025). Compressing fluid flows with nonlinear machine learning: mode decomposition, latent modeling, and flow control. Fluid Dynamics Research. 57(4). 41401–41401. 6 indexed citations
3.
4.
Fukagata, Koji, et al.. (2024). Flow control by a hybrid use of machine learning and control theory. International Journal of Numerical Methods for Heat & Fluid Flow. 34(8). 3253–3277. 4 indexed citations
5.
Fukagata, Koji, et al.. (2024). Defiltering turbulent flow fields for Lagrangian particle tracking using machine learning techniques. Physics of Fluids. 36(11). 1 indexed citations
6.
Takagi, Kazumasa, et al.. (2024). Implementation of spectral methods on Ising machines: toward flow simulations on quantum annealers. Fluid Dynamics Research. 56(6). 61401–61401.
7.
Fukagata, Koji. (2022). Towards quantum computing of turbulence. Nature Computational Science. 2(2). 68–69. 7 indexed citations
8.
Fukagata, Koji, et al.. (2021). Drag Reduction by Uniform Blowing on the Pressure Surface of an Airfoil. AIAA Journal. 60(4). 2241–2250. 7 indexed citations
9.
Fukami, Kai, et al.. (2021). Experimental velocity data estimation for imperfect particle images using machine learning. Physics of Fluids. 33(8). 69 indexed citations
10.
Maulik, Romit, Kai Fukami, Nesar Ramachandra, Koji Fukagata, & Kunihiko Taira. (2020). Probabilistic neural networks for fluid flow surrogate modeling and data recovery. Physical Review Fluids. 5(10). 93 indexed citations
11.
Maulik, Romit, Kai Fukami, Nesar Ramachandra, Koji Fukagata, & Kunihiko Taira. (2020). Probabilistic neural networks for fluid flow model-order reduction and data recovery. arXiv (Cornell University). 2 indexed citations
12.
Fukami, Kai, Taichi Nakamura, & Koji Fukagata. (2020). Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data. Physics of Fluids. 32(9). 138 indexed citations
13.
Fukagata, Koji, et al.. (2019). Numerical simulation of parallel-plate particle separator for estimation of charge distribution of PM2.5. Aerosol Science and Technology. 53(4). 394–405. 4 indexed citations
14.
Nakashima, Satoshi, et al.. (2019). Proposal of control laws for turbulent skin friction reduction based on resolvent analysis. Journal of Fluid Mechanics. 866. 810–840. 7 indexed citations
15.
Fukami, Kai, Koji Fukagata, & Kunihiko Taira. (2019). Super-resolution reconstruction of turbulent flows with machine learning. Journal of Fluid Mechanics. 870. 106–120. 483 indexed citations breakdown →
16.
Nakashima, Satoshi, Koji Fukagata, & Mitul Luhar. (2017). Assessment of suboptimal control for turbulent skin friction reduction via resolvent analysis. Journal of Fluid Mechanics. 828. 496–526. 21 indexed citations
17.
Fukagata, Koji, et al.. (2015). Turbulent boundary layer control at moderate Reynolds numbers by means of uniform blowing/suction. Bulletin of the American Physical Society. 1 indexed citations
18.
TAKAISHI, Takehisa, et al.. (2012). 123 A study on control method of flow around pantograph panhead with plasma actuator by using LES. 2012.22(0). 93–96. 1 indexed citations
19.
Fukagata, Koji, et al.. (2011). System Engineering Analysis and Optimization of a Parabolic Flight Experiment for Thermophysical Property Measurement under Microgravity. 28(2). 1 indexed citations
20.
Fukushima, Naoya, et al.. (2003). TED-AJ03-582 NUMERICAL AND EXPERIMENTAL STUDY ON TURBULENT THERMAL MIXING IN A T-JUNCTION FLOW. 2003(6). 249. 3 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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