  Chemical kinetics mechanisms are essential for understanding, analyzing, and
simulating complex combustion phenomena. In this study, a Neural Ordinary
Differential Equation (Neural ODE) framework is employed to optimize kinetics
parameters of reaction mechanisms. Given experimental or high-cost simulated
observations as training data, the proposed algorithm can optimally recover the
hidden characteristics in the data. Different datasets of various sizes, types,
and noise levels are tested. A classic toy problem of stiff Robertson ODE is
first used to demonstrate the learning capability, efficiency, and robustness
of the Neural ODE approach. A 41-species, 232-reactions JP-10 skeletal
mechanism and a 34-species, 121-reactions n-heptane skeletal mechanism are then
optimized with species' temporal profiles and ignition delay times,
respectively. Results show that the proposed algorithm can optimize stiff
chemical models with sufficient accuracy and efficiency. It is noted that the
trained mechanism not only fits the data perfectly but also retains its
physical interpretability, which can be further integrated and validated in
practical turbulent combustion simulations.
