Turbulence in fluids, gases, and plasmas remains an open problem of both
practical and fundamental importance. Its irreducible complexity usually cannot
be tackled computationally in a brute-force style. Here, we combine Large Eddy
Simulation (LES) techniques with Machine Learning (ML) to retain only the
largest dynamics explicitly, while small-scale dynamics are described by an
ML-based sub-grid-scale model. Applying this novel approach to self-driven
plasma turbulence allows us to remove large parts of the inertial range,
reducing the computational effort by about three orders of magnitude, while
retaining the statistical physical properties of the turbulent system