  The accurate prediction of small scales in underresolved flows is still one
of the main challenges in predictive simulations of complex configurations.
Over the last few years, data-driven modeling has become popular in many fields
as large, often extensively labeled datasets are now available and training of
large neural networks has become possible on graphics processing units (GPUs)
that speed up the learning process tremendously. In fact, the successful
application of deep neural networks in fluid dynamics, such as for
underresolved reactive flows, is still challenging. This work advances the
recently introduced PIESRGAN to reactive finite-rate-chemistry flows. However,
since combustion chemistry typically acts on the smallest scales, the original
approach needs to be extended. Therefore, the modeling approach of PIESRGAN is
modified to accurately account for the challenges in the context of laminar
finite-rate-chemistry flows. The modified PIESRGAN-based model gives good
agreement in a priori and a posteriori tests in a laminar lean premixed
combustion setup. Furthermore, a reduced PIESRGAN-based model is presented that
solves only the major species on a reconstructed field and employs PIERSGAN
lookup for the remaining species, utilizing staggering in time. The advantages
of the discriminator-supported training are shown, and the usability of the new
model demonstrated in the context of a model gas turbine combustor.
