An Efficient Deep Learning Technique for the Navier-Stokes Equations: Application to Unsteady Wake Flow Dynamics

Understand

We present an efficient deep learning technique for the model reduction of the Navier-Stokes equations for unsteady flow problems.

• The proposed technique relies on the Convolutional Neural Network (CNN) and the stochastic gradient descent method.
• Of particular interest is to predict the unsteady fluid forces for different bluff body shapes at low Reynolds number.
• The discrete convolution process with a nonlinear rectification is employed to approximate the mapping between the bluff-body shape and the fluid forces.