A Full Order, Reduced Order and Machine Learning Model Pipeline for Efficient Prediction of Reactive Flows

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