most citedNonlinear mode decomposition with convolutional neural networks for fluid dynamics

360 citations · 688 across the 3 of their papers we have counts for

collaborators

3 papers

physics.flu-dyn2020★ 131 cited

Sparse identification of nonlinear dynamics with low-dimensionalized flow representations

Kai Fukami, Takaaki Murata, Kai Zhang +1

We perform a sparse identification of nonlinear dynamics (SINDy) for low-dimensionalized complex flow phenomena. We first apply the SINDy with two regression methods, the threshold…

physics.flu-dyn2020★ 197 cited

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…

physics.comp-ph2019★ 360 cited

Nonlinear mode decomposition with convolutional neural networks for fluid dynamics

Takaaki Murata, Kai Fukami, Koji Fukagata

We present a new nonlinear mode decomposition method to visualize the decomposed flow fields, named the mode decomposing convolutional neural network autoencoder (MD-CNN-AE). The p…