Construction of low-dimensional system reproducing low-Reynolds-number turbulence by machine learning

Abstract

In a dissipative system, there exists the (global) attractor which has finite fractal dimensions. The flow on the attractor can be parametrized by a finite number of parameters (Temmam 1987). Using machine learning we demonstrate how to construct precise low-dimensional governing equations which are valid in some range of Reynolds number for low-Reynolds-number turbulence in plane Couette flow.