2020
DOI: 10.48550/arxiv.2006.06977
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Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data

Kai Fukami,
Taichi Nakamura,
Koji Fukagata

Abstract: We propose a customized convolutional neural network based autoencoder called a hierarchical autoencoder, which allows us to extract nonlinear autoencoder modes of flow fields while preserving the contribution order of the latent vectors. As preliminary tests, the proposed method is first applied to a cylinder wake at ReD = 100 and its transient process. It is found that the proposed method can extract the features of these laminar flow fields as the latent vectors while keeping the order of their energy conte… Show more

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