most citedNonlinear mode decomposition with convolutional neural networks for fluid dynamics

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

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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…