Physics - Informed Machine Learning for Reduced Space Chemical Kinetics

Modeling detailed chemical kinetics stands as a primary challenge in combustion simulations. Recent machine learning (ML) approaches aim to accelerate chemical kinetics integration, though their application is often limited to simpler reaction mechanisms. This study presents a novel framework to enforce physical constraints, specifically total mass and elemental conservation, into the training of ML models for reduced-space chemical kinetics of large and complex reaction mechanisms. Given the strong correlation between full and reduced solution vectors, our method utilizes a small neural network to establish an accurate and physically consistent mapping. By leveraging this mapping, we enforce physical constraints in the training process of the ML model for reduced space chemical kinetics. The framework is demonstrated here with the chemical kinetics of CH4 oxidation. The resulting solution vectors from our Deep Operator Networks-based approach are not only accurate but also align more consistently with physical laws.