Physics-Informed Automated Discovery of Kinetic Models

The industrialization of catalytic processes requires reliable kinetic models for design, optimization, and control. While white box models are preferred for their interpretability, they demand considerable time and expertise for their construction. This research enhances the ADoK-S framework by embedding prior expert knowledge using mathematical constraints and integrating uncertainty quantification. The improved methodology consists of: (I) a genetic programming algorithm with constraints to produce physically coherent candidate models, (II) a sequential optimization algorithm for parameter estimation, (III) model selection based on the Akaike information criterion (AIC), and (IV) uncertainty quantification of the chosen model�s predictions. The refined approach not only requires less data for discovering kinetic models but also ensures physically sound proposals. With the inclusion of uncertainty quantification, the method bolsters prediction reliability, and aids in safer system developments � crucial for decision-making and risk management. These improvements enhance data efficiency, model reliability, and position automated knowledge discovery as a real alternative to traditional kinetic modeling techniques.