Kai Fukami

12 papers Journal 12
YearRankTypeTitle / Venue / Authors
2025 J jnl
Information-theoretic machine learning for time-varying mode decomposition of separated airfoil wakes.
CoRR
Kai Fukami, Ryo Araki
2024 J jnl
Phase autoencoder for limit-cycle oscillators.
CoRR
Koichiro Yawata, Kai Fukami, Kunihiko Taira, Hiroya Nakao
2024 J jnl
Reconstructing Three-Dimensional Bluff Body Wake from Sectional Flow Fields with Convolutional Neural Networks.
SN Comput. Sci.
Mitsuaki Matsuo, Kai Fukami, Taichi Nakamura, Masaki Morimoto, Koji Fukagata
2024 J jnl
Single-snapshot machine learning for turbulence super resolution.
CoRR
Kai Fukami, Kunihiko Taira
2023 J jnl
Grasping Extreme Aerodynamics on a Low-Dimensional Manifold.
CoRR
Kai Fukami, Kunihiko Taira
2023 J jnl
Image and video compression of fluid flow data.
CoRR
Vishal Anatharaman, Jason Feldkamp, Kai Fukami, Kunihiko Taira
2023 J jnl
Super-Resolution Analysis via Machine Learning: A Survey for Fluid Flows.
CoRR
Kai Fukami, Koji Fukagata, Kunihiko Taira
2022 J jnl
Generalization techniques of neural networks for fluid flow estimation.
Neural Comput. Appl.
Masaki Morimoto, Kai Fukami, Kai Zhang, Koji Fukagata
2021 J jnl
Assessments of model-form uncertainty using Gaussian stochastic weight averaging for fluid-flow regression.
CoRR
Masaki Morimoto, Kai Fukami, Romit Maulik, Ricardo Vinuesa, Koji Fukagata
2021 J jnl
Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning.
CoRR
Kai Fukami, Romit Maulik, Nesar Ramachandra, Koji Fukagata, Kunihiko Taira
2021 J jnl
Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning.
Nat. Mach. Intell.
Kai Fukami, Romit Maulik, Nesar Ramachandra, Koji Fukagata, Kunihiko Taira
2021 J jnl
Model Order Reduction with Neural Networks: Application to Laminar and Turbulent Flows.
SN Comput. Sci.
Kai Fukami, Kazuto Hasegawa, Taichi Nakamura, Masaki Morimoto, Koji Fukagata