A Surrogate Reduced Order Model of the Unsteady Advection Dominant Problems Based on Combination of Deep Autoencoders-LSTM and POD (Advances in Applied Mathematics and Mechanics, 2024)
Model Order Reduction is an approximation of the main system so thatthe simplified system retains important features of the main system. Deep learning technology is a recent breakthrough in artificial neural networks that can findmore hidden information from the data. In this paper, a non-intrusive reduced order model (NIROM) based on combining deep neural networks (DNNs) and PODabilities, namely FAE-CAE-LSTM is presented. This method combines the obtainedfeatures based on Fully connected autoencoders (FAE), Convolutional autoencoders(CAE), and POD and then, a deep Long short-term memory network is trained by obtained features to predict the pressure and velocity fields at future time instances. Weinvestigate the performance of the proposed methodology by solving two well-knowncanonical cases: a strong shear flow exhibiting the Kelvin–Helmholtz instability, andflow past a cylinder. The performance of the proposed FAE-CAE-LSTM method in future state prediction of the flow is compared with other NIROM methods such as CAE-LSTM, autoencoder-LSTM, autoencoder-DMD and POD-RNN based models. Resultsshow that the FAE-CAE-LSTM method is considerably capable of predicting fluid flowevolution and obtains the best results in the prediction of the pressure and velocityfields in future time instances.