  We present an integrated approach for the use of simulated data from full
order discretization as well as projection-based Reduced Basis reduced order
models for the training of machine learning approaches, in particular Kernel
Methods, in order to achieve fast, reliable predictive models for the chemical
conversion rate in reactive flows with varying transport regimes.
