Learning about systems using machine learning: Towards more data-driven feedback loops

Machine Learning (ML) has demonstrated great potentials for constructing new knowledge, or improving already established knowledge. Reflecting this trend, the paper lends support to the discussion of why and how should ML support the practice of modeling and simulation? Subsequently, the study goes through a use case in relation to healthcare, which aims to provide a practical perspective for integrating simulation models with data-driven insights learned by ML models. Through a realistic scenario, we utilise ML clustering in order to learn about the system's structure and behaviour under study. The insights gained by the clustering model are then utilised to build a System Dynamics model. Recognizing its current limitations, the study is believed to serve as a kernel towards promoting further integration between simulation modeling and ML.