Kai Fukami

发表

Super-resolution reconstruction of turbulent flows with machine learning

K. Taira, Kai Fukami, K. Fukagata, 2018, Journal of Fluid Mechanics.

Convolutional neural networks for fluid flow analysis: toward effective metamodeling and low dimensionalization

Kai Fukami, K. Fukagata, Masaki Morimoto, 2021, Theoretical and Computational Fluid Dynamics.

Nonlinear mode decomposition for fluid dynamics

Kai Fukami, K. Fukagata, Takaaki Murata, 2019 .

Inserting machine-learned virtual wall velocity for large-eddy simulation of turbulent channel flows

Kai Fukami, K. Fukagata, Masaki Morimoto, 2021, 2106.09271.

Nonlinear mode decomposition with convolutional neural networks for fluid dynamics

Kai Fukami, K. Fukagata, Takaaki Murata, 2019, Journal of Fluid Mechanics.

Supervised convolutional network for three-dimensional fluid data reconstruction from sectional flow fields with adaptive super-resolution assistance

Kai Fukami, K. Fukagata, Masaki Morimoto, 2021, 2103.09020.

Experimental velocity data estimation for imperfect particle images using machine learning

Masaki Morimoto, Kai Fukami, Koji Fukagata, 2020, 2005.00756.

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

R. Maulik, R. Vinuesa, Kai Fukami, 2021, Physica D: Nonlinear Phenomena.

C LUES FOR N OISE R OBUSTNESS OF S TATE E STIMA TION : E RROR - CURVE Q UEST OF N EURAL N ETWORK AND L INEAR R EGRESSION

Kai Fukami, K. Fukagata, Taichi Nakamura, 2021 .

Assessment of supervised machine learning methods for fluid flows

K. Taira, Kai Fukami, K. Fukagata, 2020, Theoretical and Computational Fluid Dynamics.

Machine-learning-based reconstruction of transient vortex-airfoil wake interaction

K. Taira, Kai Fukami, Yonghong Zhong, 2022, AIAA AVIATION 2022 Forum.

Image and video compression of fluid flow data

K. Taira, Kai Fukami, Vishal Anatharaman, 2022, 2301.00078.

Image and video compression of fluid flow data

K. Taira, Kai Fukami, Vishal Anatharaman, 2022, Theoretical and Computational Fluid Dynamics.

Assessments of model-form uncertainty using Gaussian stochastic weight averaging for fluid-flow regression pdf

Masaki Morimoto, Ricardo Vinuesa, Koji Fukagata, 2021, ArXiv.

Model Order Reduction with Neural Networks: Application to Laminar and Turbulent Flows

Masaki Morimoto, Koji Fukagata, Kai Fukami, 2020, SN Computer Science.

CNN-LSTM based reduced order modeling of two-dimensional unsteady flows around a circular cylinder at different Reynolds numbers

Koji Fukagata, Kai Fukami, Takaaki Murata, 2020, Fluid Dynamics Research.

Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows

Kai Fukami, Koji Fukagata, Kunihiko Taira, 2020, Journal of Fluid Mechanics.

Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning

Kunihiko Taira, Koji Fukagata, Kai Fukami, 2021, Nature Machine Intelligence.

Probabilistic neural networks for fluid flow surrogate modeling and data recovery

Kai Fukami, Romit Maulik, Nesar Ramachandra, 2020 .

Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies of various shapes

Takaaki Murata, Kai Fukami, Koji Fukagata, 2020, Theoretical and Computational Fluid Dynamics.

Super-Resolution Analysis via Machine Learning: A Survey for Fluid Flows

K. Taira, Kai Fukami, K. Fukagata, 2023, ArXiv.

Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow

Koji Fukagata, Kai Fukami, Yusuke Nabae, 2020, Physics of Fluids.

Synthetic turbulent inflow generator using machine learning

Koji Fukagata, Kai Fukami, Ken Kawai, 2018, Physical Review Fluids.

Assessment of supervised machine learning methods for fluid flows

K. Taira, Kai Fukami, K. Fukagata, 2020, Theoretical and Computational Fluid Dynamics.

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

Kai Fukami, K. Fukagata, Taichi Nakamura, 2021, Scientific Reports.

Generalization techniques of neural networks for fluid flow estimation

Masaki Morimoto, Kai Zhang, Koji Fukagata, 2020, Neural Computing and Applications.

Experimental velocity data estimation for imperfect particle images using machine learning

Kai Fukami, K. Fukagata, Masaki Morimoto, 2020, Physics of Fluids.

Probabilistic neural networks for fluid flow model-order reduction and data recovery

Kai Fukami, Romit Maulik, Nesar Ramachandra, 2020, 2005.04271.

Comparison of linear regressions and neural networks for fluid flow problems assisted with error-curve analysis

Kai Fukami, K. Fukagata, Taichi Nakamura, 2021 .

Sparse identification of nonlinear dynamics with low-dimensionalized flow representations

Koji Fukagata, Kai Fukami, Takaaki Murata, 2020, Journal of Fluid Mechanics.

Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data

Kai Fukami, Koji Fukagata, Taichi Nakamura, 2020, Physics of Fluids.

Machine-Learning-Based Reconstruction of Turbulent Vortices From Sparse Pressure Sensors in a Pump Sump

K. Taira, Kai Fukami, M. Nohmi, 2022, Journal of Fluids Engineering.

Sparse sensor reconstruction of vortex-impinged airfoil wake with machine learning

K. Taira, Kai Fukami, Y. Zhong, 2023, Theoretical and Computational Fluid Dynamics.

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

K. Taira, Kai Fukami, K. Fukagata, 2023, Theoretical and Computational Fluid Dynamics.

Extension of CNN-LSTM based reduced order surrogate for minimal turbulent channel flow

Kai Fukami, Koji Fukagata, Yusuke Nabae, 2020 .

Data-Driven Reduced Order Modeling of Flows Around Two-Dimensional Bluff Bodies of Various Shapes

Takaaki Murata, Kai Fukami, Koji Fukagata, 2019, Volume 2: Computational Fluid Dynamics.

Super-resolution analysis with machine learning for low-resolution flow data

Kai Fukami, Koji Fukagata, Kunihiko Taira, 2019 .

Visualization of nonlinear modal structures for three-dimensional unsteady fluid flows with customized decoder design

Kai Fukami, K. Fukagata, K. Hasegawa, 2021 .

Nonlinear mode decomposition with convolutional neural networks for fluid dynamics

Kai Fukami, K. Fukagata, Takaaki Murata, 2019, Journal of Fluid Mechanics.

Grasping extreme aerodynamics on a low-dimensional manifold

K. Taira, Kai Fukami, 2023, Nature communications.

Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies of various shapes

Koji Fukagata, Kai Fukami, Takaaki Murata, 2020, Theoretical and Computational Fluid Dynamics.

Data-driven transient lift attenuation for extreme vortex gust–airfoil interactions

Kai Fukami, Hiroya Nakao, Kunihiko Taira, 2024, Journal of Fluid Mechanics.

Phase autoencoder for limit-cycle oscillators

Kai Fukami, K. Yawata, Hiroya Nakao, 2024, Chaos.

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

Kai Fukami, K. Fukagata, Masaki Morimoto, 2021, SN Comput. Sci..