Model order reduction with neural networks: Application to laminar and turbulent flows

Understand

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.