ChemNODE: A Neural Ordinary Differential Equations Approach for Chemical Kinetics Solvers

Understand

Solving for detailed chemical kinetics remains one of the major bottlenecks for computational fluid dynamics simulations of reacting flows using a finite-rate-chemistry approach.

• This has motivated the use of fully connected artificial neural networks to predict stiff chemical source terms as functions of the thermochemical state of the combustion system.
• However, due to the nonlinearities and multi-scale nature of combustion, the predicted solution often diverges from the true solution when these deep learning models are coupled with a computational fluid dynamics solver.
• This is because these approaches minimize the error during training without guaranteeing successful integration with ordinary differential equation solvers.