  Standard kernel methods for machine learning usually struggle when dealing
with large datasets. We review a recently introduced Structured Deep Kernel
Network (SDKN) approach that is capable of dealing with high-dimensional and
huge datasets - and enjoys typical standard machine learning approximation
properties. We extend the SDKN to combine it with standard machine learning
modules and compare it with Neural Networks on the scientific challenge of
data-driven prediction of closure terms of turbulent flows. We show
experimentally that the SDKNs are capable of dealing with large datasets and
achieve near-perfect accuracy on the given application.
