  We present an Auto-Encoded Reservoir-Computing (AE-RC) approach to learn the
dynamics of a 2D turbulent flow. The AE-RC consists of an Autoencoder, which
discovers an efficient manifold representation of the flow state, and an Echo
State Network, which learns the time evolution of the flow in the manifold. The
AE-RC is able to both learn the time-accurate dynamics of the flow and predict
its first-order statistical moments. The AE-RC approach opens up new
possibilities for the spatio-temporal prediction of turbulence with machine
learning.
