  Modeling of turbulent combustion system requires modeling the underlying
chemistry and the turbulent flow. Solving both systems simultaneously is
computationally prohibitive. Instead, given the difference in scales at which
the two sub-systems evolve, the two sub-systems are typically (re)solved
separately. Popular approaches such as the Flamelet Generated Manifolds (FGM)
use a two-step strategy where the governing reaction kinetics are pre-computed
and mapped to a low-dimensional manifold, characterized by a few reaction
progress variables (model reduction) and the manifold is then "looked-up"
during the run-time to estimate the high-dimensional system state by the flow
system. While existing works have focused on these two steps independently, we
show that joint learning of the progress variables and the look-up model, can
yield more accurate results. We propose a deep neural network architecture,
called ChemTab, customized for the joint learning task and experimentally
demonstrate its superiority over existing state-of-the-art methods.
