Koji Fukagata

About Koji Fukagata

Koji Fukagata, With an exceptional h-index of 40 and a recent h-index of 31 (since 2020), a distinguished researcher at Keio University, specializes in the field of Fluid Mechanics, Fluid Dynamics, Flow Control, Turbulence.

His recent articles reflect a diverse array of research interests and contributions to the field:

Theoretical and numerical analyses of turbulent plane Couette flow controlled using uniform blowing and suction

Reconstructing Three-Dimensional Bluff Body Wake from Sectional Flow Fields with Convolutional Neural Networks

Turbulent drag reduction by streamwise traveling waves of wall-normal forcing

Machine-learned reduced order modeling toward an effective flow control framework

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

Development of reduced order modeling-based linear system extracting method for efficient data handling with a minimal nonlinearity

A new perspective on skin-friction contributions in adverse-pressure-gradient turbulent boundary layers

Estimation of oscillation parameters of a circular cylinder from its downstream vorticity fields

Koji Fukagata Information

University

Position

- Department of Mechanical Engineering

Citations(all)

6748

Citations(since 2020)

4375

Cited By

3532

hIndex(all)

40

hIndex(since 2020)

31

i10Index(all)

94

i10Index(since 2020)

67

Email

University Profile Page

Google Scholar

Koji Fukagata Skills & Research Interests

Fluid Mechanics

Fluid Dynamics

Flow Control

Turbulence

Top articles of Koji Fukagata

Title

Journal

Author(s)

Publication Date

Theoretical and numerical analyses of turbulent plane Couette flow controlled using uniform blowing and suction

International Journal of Heat and Fluid Flow

Yusuke Nabae

Koji Fukagata

2024/4/1

Reconstructing Three-Dimensional Bluff Body Wake from Sectional Flow Fields with Convolutional Neural Networks

SN Computer Science

Mitsuaki Matsuo

Kai Fukami

Taichi Nakamura

Masaki Morimoto

Koji Fukagata

2024/3/7

Turbulent drag reduction by streamwise traveling waves of wall-normal forcing

Koji Fukagata

Kaoru Iwamoto

Yosuke Hasegawa

2024/1/19

Machine-learned reduced order modeling toward an effective flow control framework

Bulletin of the American Physical Society

Hiroshi Omichi

Takeru Ishize

Koji Fukagata

2023/11/20

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

Kai Fukami

Koji Fukagata

Kunihiko Taira

2023/8

Development of reduced order modeling-based linear system extracting method for efficient data handling with a minimal nonlinearity

Bulletin of the American Physical Society

Takeru Ishize

Koji Fukagata

2023/11/20

A new perspective on skin-friction contributions in adverse-pressure-gradient turbulent boundary layers

International Journal of Heat and Fluid Flow

Marco Atzori

Fermín Mallor

Ramón Pozuelo

Koji Fukagata

Ricardo Vinuesa

...

2023/6/1

Estimation of oscillation parameters of a circular cylinder from its downstream vorticity fields

Bulletin of the American Physical Society

Hikaru Chida

Kai Zhang

Koji Fukagata

2023/11/20

機械学習による流れのはく離検出手法の構築

後藤陸, 石瀬健, 深潟康二

2023

Semi-supervised machine learning model for Lagrangian state estimation

Bulletin of the American Physical Society

Reno Miura

Koji Fukagata

2023/11/20

DNS データを用いない機械学習による粒子画像流速測定法の信頼性向上

ながれ: 日本流体力学会誌= Nagare: journal of Japan Society of Fluid Mechanics

大道浩志, 千田晃, 石瀬健, 松尾光昭, 深潟康二

2023

Flow control by a hybrid use of machine learning and control theory

arXiv preprint arXiv:2311.08624

Takeru Ishize

Hiroshi Omichi

Koji Fukagata

2023/11/15

Machine learning based dimension reduction for a stable modeling of periodic flow phenomena

arXiv preprint arXiv:2311.08765

Hiroshi Omichi

Takeru Ishize

Koji Fukagata

2023/11/15

Reduced order modeling of fluid flows using convolutional neural networks

Journal of Fluid Science and Technology

Koji Fukagata

2023

畳み込みニューラルネットワークを用いた流体場の低次元化と欠損情報推定

日本風工学会誌

深潟康二

2022

基礎的な流れ場に対する機械学習の応用

日本ガスタービン学会誌

深潟康二

2022

Identifying key differences between linear stochastic estimation and neural networks for fluid flow regressions

Scientific reports

Taichi Nakamura

Kai Fukami

Koji Fukagata

2022/3/8

Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression

Physica D: Nonlinear Phenomena

Masaki Morimoto

Kai Fukami

Romit Maulik

Ricardo Vinuesa

Koji Fukagata

2022/11/15

Model-form uncertainty quantification in neural-network-based fluid-flow estimation

ながれ: 日本流体力学会誌= Nagare: journal of Japan Society of Fluid Mechanics

森本将生, 深見開, 深潟康二

2022

Generalization techniques of neural networks for fluid flow estimation

Neural Computing and Applications

Masaki Morimoto

Kai Fukami

Kai Zhang

Koji Fukagata

2022/3

See List of Professors in Koji Fukagata University(Keio University)

Co-Authors

academic-engine