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Koji Fukagata

Koji Fukagata

D-Index & Metrics

Mechanical and Aerospace Engineering

D-Index
36
Citations
6959
World Ranking
2476
National Ranking
58

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
Yuji Suzuki
Yuji Suzuki University of Tokyo
François Gallaire
François Gallaire École Polytechnique Fédérale de Lausanne
P. Henrik Alfredsson
P. Henrik Alfredsson Royal Institute of Technology

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