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physics.comp-ph2020
Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data
Kai Fukami, Taichi Nakamura, Koji Fukagata
We propose a customized convolutional neural network based autoencoder called a hierarchical autoencoder, which allows us to extract nonlinear autoencoder modes of flow fields whil…
physics.comp-ph2019
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…