  In this paper, we present a novel model-agnostic machine learning technique
to extract a reduced thermochemical model for reacting hypersonic flows
simulation. A first simulation gathers all relevant thermodynamic states and
the corresponding gas properties via a given model. The states are embedded in
a low-dimensional space and clustered to identify regions with different levels
of thermochemical (non)-equilibrium. Then, a surrogate surface from the reduced
cluster-space to the output space is generated using radial-basis-function
networks. The method is validated and benchmarked on a simulation of a
hypersonic flat-plate boundary layer with finite-rate chemistry. The gas
properties of the reactive air mixture are initially modeled using the
open-source Mutation++ library. Substituting Mutation++ with the light-weight,
machine-learned alternative improves the performance of the solver by 50% while
maintaining overall accuracy.
