  Unsteady fluid systems are nonlinear high-dimensional dynamical systems that
may exhibit multiple complex phenomena both in time and space. Reduced Order
Modeling (ROM) of fluid flows has been an active research topic in the recent
decade with the primary goal to decompose complex flows to a set of features
most important for future state prediction and control, typically using a
dimensionality reduction technique. In this work, a novel data-driven technique
based on the power of deep neural networks for reduced order modeling of the
unsteady fluid flows is introduced. An autoencoder network is used for
nonlinear dimension reduction and feature extraction as an alternative for
singular value decomposition (SVD). Then, the extracted features are used as an
input for long short-term memory network (LSTM) to predict the velocity field
at future time instances. The proposed autoencoder-LSTM method is compared with
non-intrusive reduced order models based on dynamic mode decomposition (DMD)
and proper orthogonal decomposition (POD). Moreover, an autoencoder-DMD
algorithm is introduced for reduced order modeling, which uses the autoencoder
network for dimensionality reduction rather than SVD rank truncation. Results
show that the autoencoder-LSTM method is considerably capable of predicting
fluid flow evolution, where higher values for coefficient of determination
$R^{2}$ are obtained using autoencoder-LSTM compared to other models.
