  The fast and accurate prediction of unsteady flow becomes a serious challenge
in fluid dynamics, due to the high-dimensional and nonlinear characteristics. A
novel hybrid deep neural network (DNN) architecture was designed to capture the
unsteady flow spatio-temporal features directly from the high-dimensional
unsteady flow fields. The hybrid deep neural network is constituted by the
convolutional neural network (CNN), convolutional Long Short Term Memory neural
network (ConvLSTM) and deconvolutional neural network (DeCNN). The flow around
a cylinder at various Reynolds numbers and the flow around an airfoil at higher
Reynolds number are carried out to establish the datasets used to train the
networks separately. The trained hybrid DNNs were then tested by the prediction
of the flow fields at future occasions. The predicted flow fields using the
trained hybrid DNNs are in good agreement with the flow fields calculated
directly by the computational fluid dynamic solver.
