  In this article we detail the use of machine learning for spatiotemporally
dynamic turbulence model classification and hybridization for the large eddy
simulations (LES) of turbulence. Our predictive framework is devised around the
determination of local conditional probabilities for turbulence models that
have varying underlying hypotheses. As a first deployment of this learning, we
classify a point on our computational grid as that which requires the
functional hypothesis, the structural hypothesis or no modeling at all. This
ensures that the appropriate model is specified from \emph{a priori} knowledge
and an efficient balance of model characteristics is obtained in a particular
flow computation. In addition, we also utilize the conditional probability
predictions of the same machine learning to blend turbulence models for another
hybrid closure. Our test-case for the demonstration of this concept is given by
Kraichnan turbulence which exhibits a strong interplay of enstrophy and energy
cascades in the wave number domain. Our results indicate that the proposed
methods lead to robust and stable closure and may potentially be used to
combine the strengths of various models for complex flow phenomena prediction.
