Predicting 2D Turbulence

Abstract

Prediction is a fundamental objective of science. It is more difficult for chaotic and complex systems like turbulence. Here we use information theory to quantify spatial prediction using experimental data from a turbulent soap film. At high Reynolds number $Re$ where a cascade exists, turbulence is becoming easier to predict as the inertial range broadens. A transition corresponding to the emergence of a cascade at low $Re$ is detected by looking at turbulence through prediction.