  A combustion chemistry acceleration scheme is developed based on deep
operator networks (DeepONets). The scheme is based on the identification of
combustion reaction dynamics through a modified DeepOnet architecture such that
the solutions of thermochemical scalars are projected to new solutions in small
and flexible time increments. The approach is designed to efficiently implement
chemistry acceleration without the need for computationally expensive
integration of stiff chemistry. An additional framework of latent-space
dynamics identification with modified DeepOnet is also proposed which enhances
the computational efficiency and widens the applicability of the proposed
scheme. The scheme is demonstrated on simple chemical kinetics of hydrogen
oxidation to more complex chemical kinetics of n-dodecane high- and
low-temperature oxidations. The proposed framework accurately learns the
chemical kinetics and efficiently reproduces species and temperature temporal
profiles corresponding to each application. In addition, a very large speed-up
with a great extrapolation capability is also observed with the proposed
scheme.
