Kai Fukami

2.9k total citations · 5 hit papers
35 papers, 1.9k citations indexed

About

Kai Fukami is a scholar working on Statistical and Nonlinear Physics, Computational Mechanics and Aerospace Engineering. According to data from OpenAlex, Kai Fukami has authored 35 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Statistical and Nonlinear Physics, 27 papers in Computational Mechanics and 14 papers in Aerospace Engineering. Recurrent topics in Kai Fukami's work include Model Reduction and Neural Networks (27 papers), Fluid Dynamics and Turbulent Flows (26 papers) and Aerodynamics and Acoustics in Jet Flows (8 papers). Kai Fukami is often cited by papers focused on Model Reduction and Neural Networks (27 papers), Fluid Dynamics and Turbulent Flows (26 papers) and Aerodynamics and Acoustics in Jet Flows (8 papers). Kai Fukami collaborates with scholars based in United States, Japan and Sweden. Kai Fukami's co-authors include Koji Fukagata, Kunihiko Taira, Takaaki Murata, Taichi Nakamura, Kazuto Hasegawa, Romit Maulik, Nesar Ramachandra, Kai Zhang, Aditya Nair and Hiroya Nakao and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Journal of Fluid Mechanics.

In The Last Decade

Kai Fukami

35 papers receiving 1.8k citations

Hit Papers

5 papers align trajectories

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.

Super-resolution reconstruction of turbulent flows with machine learning

2019 483 citations Kai Fukami, Koji Fukagata et al. Journal of Fluid Mechanics profile →
Nonlinear mode decomposition with convolutional neural networks for fluid dynamics

2019 222 citations Takaaki Murata, Kai Fukami et al. Journal of Fluid Mechanics profile →
Assessment of supervised machine learning methods for fluid flows

2020 175 citations Kai Fukami, Koji Fukagata et al. Theoretical and Computational Fluid Dynamics profile →
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 →
Super-resolution analysis via machine learning: a survey for fluid flows

2023 94 citations Kai Fukami, Koji Fukagata et al. Theoretical and Computational Fluid Dynamics profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Kai Fukami United States	16	1.3k	1.1k	504	278	202	35	1.9k
Romit Maulik United States	20	850 0.6×	956 0.8×	299 0.6×	73 0.3×	201 1.0×	80	1.6k
Dunhui Xiao China	29	773 0.6×	1.1k 1.0×	338 0.7×	62 0.2×	211 1.0×	68	2.2k
Soledad Le Clainche Spain	24	1.2k 0.9×	885 0.8×	544 1.1×	56 0.2×	69 0.3×	96	1.8k
Thomas Bewley United States	27	2.0k 1.5×	721 0.6×	691 1.4×	130 0.5×	294 1.5×	120	2.9k
Xiaowei Jin China	8	675 0.5×	757 0.7×	347 0.7×	58 0.2×	65 0.3×	21	1.2k
Andrew Kurzawski United States	10	768 0.6×	660 0.6×	328 0.7×	49 0.2×	110 0.5×	20	1.3k
Ameya D. Jagtap United States	15	1.3k 1.0×	2.6k 2.3×	618 1.2×	137 0.5×	132 0.7×	30	3.5k
Nils Thuerey Germany	23	1.1k 0.8×	441 0.4×	243 0.5×	567 2.0×	70 0.3×	67	1.8k
Zhicheng Wang China	9	533 0.4×	1.1k 0.9×	318 0.6×	74 0.3×	78 0.4×	25	2.0k
Scott T. M. Dawson United States	14	2.0k 1.5×	1.3k 1.2×	998 2.0×	52 0.2×	111 0.5×	48	2.7k

Countries citing papers authored by Kai Fukami

Since Specialization

Citations

This map shows the geographic impact of Kai Fukami'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 Kai Fukami with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kai Fukami more than expected).

Fields of papers citing papers by Kai Fukami

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kai Fukami. 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 Kai Fukami. The network helps show where Kai Fukami may publish in the future.

Co-authorship network of co-authors of Kai Fukami

This figure shows the co-authorship network connecting the top 25 collaborators of Kai Fukami. A scholar is included among the top collaborators of Kai Fukami 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 Kai Fukami. Kai Fukami is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

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

Fukami, Kai, et al.. (2025). Extreme vortex-gust airfoil interactions at Reynolds number 5000. Physical Review Fluids. 10(8). 1 indexed citations

Fukami, Kai & Ryo Araki. (2025). Information-Theoretic Machine Learning for Time-Varying Mode Decomposition of Separated Aerodynamic Flows. AIAA Journal. 64(2). 605–613. 1 indexed citations

Fukami, Kai, Hiroya Nakao, & Kunihiko Taira. (2024). Data-driven transient lift attenuation for extreme vortex gust–airfoil interactions. Journal of Fluid Mechanics. 992. 15 indexed citations

Fukami, Kai & Kunihiko Taira. (2024). Single-snapshot machine learning for super-resolution of turbulence. Journal of Fluid Mechanics. 1001. 13 indexed citations

Fukami, Kai, et al.. (2024). Aerodynamics-guided machine learning for design optimization of electric vehicles. SHILAP Revista de lepidopterología. 3(1). 174–174. 7 indexed citations

Fukami, Kai & Kunihiko Taira. (2024). Data-Driven Modeling, Sensing, and Control of Extreme Vortex-Airfoil Interactions. 1 indexed citations

Fukami, Kai, et al.. (2023). Sparse sensor reconstruction of vortex-impinged airfoil wake with machine learning. Theoretical and Computational Fluid Dynamics. 37(2). 269–287. 23 indexed citations

Fukami, Kai, Koji Fukagata, & Kunihiko Taira. (2023). Super-resolution analysis via machine learning: a survey for fluid flows. Theoretical and Computational Fluid Dynamics. 37(4). 421–444. 94 indexed citations breakdown →

10.

Fukami, Kai, et al.. (2023). Image and video compression of fluid flow data. Theoretical and Computational Fluid Dynamics. 37(1). 61–82. 7 indexed citations

11.

Fukami, Kai & Kunihiko Taira. (2023). Grasping extreme aerodynamics on a low-dimensional manifold. Nature Communications. 14(1). 6480–6480. 50 indexed citations

12.

Nakamura, Taichi, Kai Fukami, & Koji Fukagata. (2022). Identifying key differences between linear stochastic estimation and neural networks for fluid flow regressions. Scientific Reports. 12(1). 3726–3726. 22 indexed citations

13.

Fukami, Kai, et al.. (2021). Experimental velocity data estimation for imperfect particle images using machine learning. Physics of Fluids. 33(8). 69 indexed citations

14.

Nakamura, Taichi, et al.. (2021). Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow. Physics of Fluids. 33(2). 144 indexed citations breakdown →

15.

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

16.

Hasegawa, Kazuto, Kai Fukami, Takaaki Murata, & Koji Fukagata. (2020). CNN-LSTM based reduced order modeling of two-dimensional unsteady flows around a circular cylinder at different Reynolds numbers. Fluid Dynamics Research. 52(6). 65501–65501. 96 indexed citations

17.

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

18.

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

19.

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 →

20.

Murata, Takaaki, Kai Fukami, & Koji Fukagata. (2019). Nonlinear mode decomposition with convolutional neural networks for fluid dynamics. Journal of Fluid Mechanics. 882. 222 indexed citations breakdown →

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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