Reference Architectures for Digital Twins: Results of a Literature Review

Context: Digital Twin (DT) technology has emerged as a key enabler in the development of contemporary systems, offering dynamic, real-time representations of physical assets and processes. By facilitating advanced monitoring, simulation, and predictive analysis, Digital Twins contribute to enhanced operational efficiency, informed decision-making, and continuous innovation. Their applicability spans a wide range of domains, including manufacturing, healthcare, transportation, and smart infrastructure. Problem: Despite the growing interest of industry in using DT for improving their processes, there is a lack of understanding of the software architectures for DT solutions. Goal: This work aims to provide an overview of reference architectures for DT, characterizing its primary stakeholders, the engineering approaches used in its creation, the industry involvement in its development, and the concerns that guide its design. Method: A rapid literature review was executed, using Scopus and Google Scholar as primary databases. Results: From an initial set of 216 studies, seventeen reference architectures for DTs were found and characterized. Most architectures have been defined since 2020 for multiple domains and purposes, encompassing the involvement of various industry sectors and stakeholders. Finally, essential concerns and limitations of those architectures are discussed, paving the way for further contributions.