  Developing data-driven subgrid-scale (SGS) models for large eddy simulations
(LES) has received substantial attention recently. Despite some success,
particularly in a priori (offline) tests, challenges have been identified that
include numerical instabilities in a posteriori (online) tests and
generalization (i.e., extrapolation) of trained data-driven SGS models, for
example to higher Reynolds numbers. Here, using the stochastically forced
Burgers turbulence as the test-bed, we show that deep neural networks trained
using properly pre-conditioned (augmented) data yield stable and accurate a
posteriori LES models. Furthermore, we show that transfer learning enables
accurate/stable generalization to a flow with 10x higher Reynolds number.
