Turbulence model reduction by deep learning

Physical Review. E
R A Heinonen, P H Diamond

Abstract

A central problem of turbulence theory is to produce a predictive model for turbulent fluxes. These have profound implications for virtually all aspects of the turbulence dynamics. In magnetic confinement devices, drift-wave turbulence produces anomalous fluxes via cross-correlations between fluctuations. In this work, we introduce an alternative, data-driven method for parametrizing these fluxes. The method uses deep supervised learning to infer a reduced mean-field model from a set of numerical simulations. We apply the method to a simple drift-wave turbulence system and find a significant new effect which couples the particle flux to the local gradient of vorticity. Notably, here, this effect is much stronger than the oft-invoked shear suppression effect. We also recover the result via a simple calculation. The vorticity gradient effect tends to modulate the density profile. In addition, our method recovers a model for spontaneous zonal flow generation by negative viscosity, stabilized by nonlinear and hyperviscous terms. We highlight the important role of symmetry to implementation of the alternative method.

References

Sep 28, 2010·Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics·G Dif-PradalierC S Chang
Mar 15, 2015·Physical Review Letters·G Dif-PradalierA Storelli
May 29, 2015·Nature·Yann LeCunGeoffrey Hinton
Dec 15, 2016·Physical Review. E·Arash Ashourvan, P H Diamond
Aug 14, 2019·Physics Reports·Pankaj MehtaDavid J Schwab

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