Meta-learning innovates chemical kinetics: An efficient approach for surrogate model construction

Abstract

The construction of surrogate models is an essential step in the uncertainty quantification of combustion reaction kinetics. These models create a mapping between inputs and outputs of combustion kinetics simulations, thereby replacing the time-consuming numerical simulations of reaction kinetics and significantly lowering the computational costs for uncertainty quantification. However, in applications such as experimental design that require repeated construction of surrogate models under multiple operating conditions, the associated computational burden becomes substantial and can even limit the feasibility of the entire task. It is therefore essential to investigate cost-efficient surrogate model construction methods. Drawing inspiration from image classification in computer vision, this work introduces a meta-learning-assisted approach to efficiently construct surrogate models by leveraging the intrinsic shared features among them. By learning from a limited set of training tasks, the approach facilitates rapid creating surrogate models for new conditions with fewer samples. This is particularly beneficial for reducing computational costs since the most significant expense comes from the generation of original samples. The method has been tested in ammonia-hydrogen combustion targeting ignition delay time and laminar burning velocity. Results show that the efficiency of the surrogate model construction can be improved by a factor of eight for individual new conditions, and the total computational costs across the entire condition range can be reduced to 29 % and 37 % of the original values for the two prediction targets, respectively. Notably, dual pretraining across both prediction targets further enhances model performance. The meta-learning-assisted surrogate model construction approach is applicable across a broad range of operating conditions, requiring only minimal additional pretraining costs while offering flexible precision control based on task-specific requirements.