Subgrid Modeling of Gyrokinetic Turbulence using Machine Learning

Turbulence in magnetic confinement fusion (MCF) experiments acts as a significant catalyst for driving the radial transport of heat.  Future MCF experiments must operate in regimes that suppress turbulence in order to efficiently sustain core temperatures that facilitate fusion events.  Modeling radial transport is an inherently multiscale objective, as small-scale instabilities influence macroscale heat and particle diffusion. In the interest of decreasing the computational costs of turbulence models, we will present a machine-learned subgrid model for gyrokinetic turbulence. We have reproduced the recent reservoir computing results of J. Pathak and collaborators for the Kuramoto-Sivashinsky system in the framework of GX [1].  We will present a successful extension of that work on a spectral domain.  We will discuss ways in which this methodology can interface with coarsely-resolved numerical solutions.  

[1] J. Pathak, B. Hunt, M. Girvan, Z. Lu, and E. Ott, Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach, Phys. Rev. Lett. 120, 024102 (2018).