  A neural network is trained using simulation data from a Runge Kutta
discontinuous Galerkin (RKDG) method and a modal high order limiter. With this
methodology, we design one and two-dimensional black-box shock detection
functions. Furthermore, we describe a strategy to adapt the shock detection
function to different numerical schemes without the need of a full training
cycle and large dataset. We evaluate the performance of the neural network on a
RKDG scheme for validation. To evaluate the domain adaptation properties of
this neural network limiter, our methodology is verified on a residual
distribution scheme (RDS), both in one and two-dimensional problems, and on
Cartesian and unstructured meshes. Lastly, we report on the quality of the
numerical solutions when using a neural based shock detection method, in
comparison to more traditional limiters, as well as on the computational impact
of using this method in existing codes.
