227 citations · 427 across the 4 of their papers we have counts for
4 papers
Comparing flow-based and anatomy-based features in the data-driven study of nasal pathologies
Andrea Schillaci, Kazuto Hasegawa, Carlotta Pipolo +2
In several problems involving fluid flows, Computational Fluid Dynamics (CFD) provides detailed quantitative information, and often allows the designer to successfully optimize the…
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…
Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow
Taichi Nakamura, Kai Fukami, Kazuto Hasegawa +2
We investigate the applicability of machine learning based reduced order model (ML-ROM) to three-dimensional complex flows. As an example, we consider a turbulent channel flow at t…
Machine-learning-based reduced order modeling for unsteady flows around bluff bodies of various shapes
Kazuto Hasegawa, Kai Fukami, Takaaki Murata +1
We propose a method to construct a reduced order model with machine learning for unsteady flows. The present machine-learned reduced order model (ML-ROM) is constructed by combinin…