A digital twin (DT) of a system has to cover all relevant aspects describing the system dynamics, and in most cases, the existing knowledge and experience within these aspects will vary. This implies that while it is often possible to describe some phenomenons using knowledge-based models (KBM), other elements will have to be described using data-driven models (DDM). A full digital twin realization will therefore also require the use of methods for integrating KBM and DDM in a common seamless system that exploits the advantages of both approaches. The approach called hybrid analysis and modeling (HAM) can be used to identify methods to achieve this. This chapter describes the concept of HAM and the different categories of HAM methods, all in light of its utility toward building digital twin solutions for aquaculture. Examples of KBM, DDM, and potential HAM methods related to aquaculture production will be provided throughout the chapter to illustrate this connection. In conclusion, it is apparent that the development of digital twins for aquaculture will require the use of HAM methods to bridge knowledge gaps and eventually provide a deeper insight into system dynamics. This will also contribute to bridging the spatial and temporal scales relevant for aquaculture and systems scale analytics, i.e., from individual fish through full farms to regional scales and from daily/weekly operations through full farming cycles to series of multiple farming cycles.