Data-driven prediction of unsteady flow over a circular cylinder using deep learning

Unsteady flow fields over a circular cylinder are predicted using deep learning networks. Deep learning networks construct nonlinear mappings that allows prediction of flow fields at future occasions based on flow fields at past occasions. Deep learning networks equipped with four different loss-function configurations are trained using flow fields at Re_D = 100, 200, 300, and 400. Two networks are trained using different loss-function sets: with and without loss functions for mass and momentum conservation, and two other networks are trained using loss-function sets: with and without loss functions for mass and momentum conservation both with a loss function for adversarial training. The trained networks are employed to predict flow fields at Re_D = 500, and 3000, at which Reynolds numbers, the networks are not exposed to flow fields a priori. Results predicted by each network are compared and analyzed to identify effects of the configuration of loss functions and the use of adversarial training on the predictive performance.