Uncovering Flame Physics with Machine Learning: Application to the Reaction Rate in Hydrogen Flames

The paper uses convolutional neural networks as analytical tools rather than as substitutes for physical modeling. It examines relationships between progress-variable fields and local chemical source terms in lean premixed hydrogen flames using spatially resolved fields from direct numerical simulations. The study focuses on extracting physical information relevant to reaction rates and flame physics.