Abstract
Traditional approaches for mechanistic modelling require in-depth understanding of the underlying chemical and physical phenomena to construct reliable and predictive models. However, at early stages of development, limited experimental data, incomplete expert knowledge, and non-observable states often hinder a full understanding of the underlying mechanisms. Symbolic regression (SR) enables systematic model discovery and offers a practical route to addressing these challenges by automating the identification of interpretable model structures and the estimation of associated parameters from available data. However, structural identifiability and observability (SIO), a critical property of such models, is often overlooked in SR, thereby limiting its broader adoption and effective deployment. To address these limitations, this study proposes a comprehensive framework, which leverages scarce prior knowledge in SR and incorporates SIO analysis, offering a potential solution to capture the effects of all state variables and ensure rigorous structure of the resulting models. The proposed strategy is demonstrated on a partially observed sulfide oxidation system, demonstrating how an SIO-assured model can be systematically identified from limited data and incomplete system knowledge. Overall, this work presents a potential pathway for extending model discovery to partially observed systems and enhancing model robustness, thereby positioning SR as an alternative tool with respect to traditional kinetic modeling strategies.