  This study presents an artificial neural network and proper orthogonal
decomposition (POD)-based reduced-order model (ROM) of turbulent flow around a
finite wall-mounted square cylinder. The proposed model is suitable for
turbulent wake control applications because it can predict the dynamics of the
main features of the flow field without computing Navier-Stokes equations. Long
short-term memory neural network (LSTM NN) and bidirectional long short-term
memory neural network (BLSTM NN) are used to predict the temporal evolution of
the POD time coefficients at different planes along the height of the obstacle.
The improved delayed detached-eddy simulation (IDDES) is performed to generate
the training datasets. Transfer learning (TL) approach is utilized in the
training process by using the weights of the LSTM/BLSTM NN that are used to
predict the POD time coefficients of the planes at lower elevations to
initialize the weights of the networks at higher elevations along the height of
the obstacle. The use of TL results in a remarkable improvement in the
capability of the LSTM/BLSTM NN prediction compared with the one when the
network is initialized with random weights. BLSTM NN shows better results
compared with LSTM NN in terms of training and prediction error, indicating
that the BLSTM-POD model is more suitable to be used as a ROM for predicting
the turbulent wake. Furthermore, the temporal behavior of the time coefficients
is carefully examined using the phase space plots and Poincar$\acute{e}$
sections. The results of using different lengths of the prediction time window
showed that the prediction error of the POD time coefficients increases as the
prediction time window increases and the error increasing rate decreases with
the ranking of the POD time coefficients.
