  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 neural network and multi-layer perceptrons is
considered in this study. The AE model is assessed with four canonical fluid
flows, namely: (1) two-dimensional cylinder wake, (2) its transient process,
(3) NOAA sea surface temperature, and (4) $y-z$ sectional field of turbulent
channel flow, in terms of a number of latent modes, a choice of nonlinear
activation functions, and a number of weights contained in the AE model. We
find that the AE models are sensitive against the choice of the aforementioned
parameters depending on the target flows. Finally, we foresee the extensional
applications and perspectives of machine learning based order reduction for
numerical and experimental studies in fluid dynamics community.
