134 citations · 405 across the 7 of their papers we have counts for
7 papers
Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression
Masaki Morimoto, Kai Fukami, Romit Maulik +2
We use Gaussian stochastic weight averaging (SWAG) to assess the model-form uncertainty associated with neural-network-based function approximation relevant to fluid flows. SWAG ap…
Inserting machine-learned virtual wall velocity for large-eddy simulation of turbulent channel flows
Naoki Moriya, Kai Fukami, Yusuke Nabae +3
We propose a supervised-machine-learning-based wall model for coarse-grid wall-resolved large-eddy simulation (LES). Our consideration is made on LES of turbulent channel flows wit…
Reconstructing three-dimensional bluff body wake from sectional flow fields with convolutional neural networks
Mitsuaki Matsuo, Kai Fukami, Taichi Nakamura +2
The recent development of high-performance computing enables us to generate spatio-temporal high-resolution data of nonlinear dynamical systems and to analyze them for a deeper und…
Convolutional neural networks for fluid flow analysis: toward effective metamodeling and low-dimensionalization
Masaki Morimoto, Kai Fukami, Kai Zhang +2
We focus on a convolutional neural network (CNN), which has recently been utilized for fluid flow analyses, from the perspective on the influence of various operations inside it by…
Generalization techniques of neural networks for fluid flow estimation
Masaki Morimoto, Kai Fukami, Kai Zhang +1
We demonstrate several techniques to encourage practical uses of neural networks for fluid flow estimation. In the present paper, three perspectives which are remaining challenges…
Model order reduction with neural networks: Application to laminar and turbulent flows
Kai Fukami, Kazuto Hasegawa, Taichi Nakamura +2
We investigate the capability of neural network-based model order reduction, i.e., autoencoder (AE), for fluid flows. As an example model, an AE which comprises of a convolutional…
Experimental velocity data estimation for imperfect particle images using machine learning
Masaki Morimoto, Kai Fukami, Koji Fukagata
We propose a method using supervised machine learning to estimate velocity fields from particle images having missing regions due to experimental limitations. As a first example, a…