Accelerated evolution of Burgers turbulence with coarse projective integration and deep learning

Abstract

Simulating turbulence to stationarity is a major bottleneck in many engineering problems of practical importance. The problem can be cast as a multiscale problem involving energy redistribution processes that take place on the long large eddy turnover time scale and chaotic processes that take the much shorter time scale of the turbulent fluctuations. But the absence of a way to perform super-resolution reconstructions of the instantaneous velocity field from its lower dimensional moments has prevented the implementation of multiscale computational approaches for accelerating turbulence simulations. Here, we use an encoder-decoder recurrent neural network to learn the relationship between the instantaneous velocity field and energy spectrum for the one-dimensional stochastic Burgers equation, a common toy model of turbulence. We use the model to reconstruct the velocity field from instantaneous energy spectra in an equation-free coarse projective integration multiscale simulation scheme using a velocity-energy, fine-coarse multiscale framework. The multiscale-machine learning approach put forth here recovers the final statistically stationary turbulent Burgers velocity field up to 442 times faster in wall clock time than using direct numerical simulation alone, with 3-digit accuracy.