Coupling mechanistic and data-driven models by means of neural differential equations to incorporate unmodeled dynamics

Abstract

Good predictive models are essential in modern chemical industry for control and optimization. Mechanistic models, incorporating physical and empirical knowledge, are dominant. However, the incorporated knowledge is inherently a simplification of reality, resulting in model structure uncertainty. Moreover, gathering the necessary knowledge is time-consuming and might be unfeasible for complex poorly-understood processes. With an ever-increasing amount of data available, data-driven methods are becoming more attractive. In these models, the structure is not explicitly specified, but rather determined by searching for relationships in the available data. Given sufficient and representative data, these models can make highly accurate predictions, unconstrained by any assumptions made. Therefore, they are particularly powerful for learning complex and poorly understood dynamics. The downside is their complete lack of interpretability. Moreover, as representative data is needed, they fail to extrapolate into regions not seen before