Revealing the state space of turbulence using machine learning

Abstract

We train deep convolutional autoencoders to learn highly efficient embeddings of two-dimensional turbulence. We define a new technique, latent Fourier analysis, that decomposes these representations into a set of interpretable recurrent patterns, and show how these recurrent patterns are closely related to the simple invariant solutions populating the turbulent attractor. By examining a series of bursting episodes with this framework we are able to identify large numbers of new simple invariant solutions that characterize these events and which have avoided previous detection methods.

Topics & Concepts