## 2 Background and Related Work  
Despite the research around digital, high-fidelity copies of physical objects had flourished in the 1980s, the term \"Digital Twins\" is reported to first be used by [PERSON] in 2003 at the University of Michigan [6]. Since its inception, this idea has increasingly garnered attention for its ability to create a digital depiction of a physical object. Although there is no unique, globally accepted definition of the DT concept, there are some aspects of it that is agreed upon by many researchers and practitioners [7]. Initially embraced in the manufacturing sector, the adoption of Digital Twins (DT) has extended into the domains of the Internet of Things (IoT) and cyber-physical systems (CPSs) [8]. Furthermore, it has captured the attention of various technical communities and professionals spanning diverse industries. These stakeholders have identified shared aspects between their existing methodologies, concepts, and needs. As a result, the concept of digital twins has evolved and expanded, leading to a diverse interpretation influenced by the particular domain and intended purpose. The literature is full of attempts to give a formal and exhaustive definition of DT [9; 10; 11; 12; 13]. In this article, we will stick to the definition proposed by Ref. [14], as we believe it embodies the concept of DT that best fits our aim: \"A DT is a comprehensive digital representation of an individual product. It includes the properties, conditions, and behavior(s) of the real-life object through models and data. A DT is a set of realistic models that can simulate an object's behavior in the deployed environment. The DT represents and reflects its physical twin and remains its virtual counterpart across the object's entire lifecycle\". More specifically, _Industrial Digital Twins_ (IDTs) refer to virtual representations of physical industrial assets, processes, and systems. An IDT serves as a dynamic, real-time digital counterpart of a physical asset or process, enabling monitoring, analysis, and optimization within a virtual environment. For a comprehensive list of IDT definitions, readers may consult Ref. [15]. Examples of industries that benefit from IDTs include manufacturing, energy (power plants, oil and gas facilities), transportation (aircraft, trains,vehicles), healthcare (patient monitoring and treatment optimization), and more. Here is a non-exhaustive list of potential areas of application of IDTs in industrial scenarios:  
* Real-time monitoring and data collection. IDTs continuously gather data from sensors and other sources in the real world. This data is used to update the virtual representation, ensuring that it closely reflects the current state of the physical asset or process. * Predictive analysis involves utilizing historical data and advanced analytics within IDTs to forecast future behavior and anticipate potential issues. This capability facilitates proactive maintenance, ultimately minimizing downtime. * Remote operation and control. By way of IDTs, operators can remotely monitor and control physical assets, even in challenging or hazardous environments. This proves especially beneficial for sectors such as energy, oil and gas, and manufacturing. * Optimization and testing. IDTs enable the testing of various scenarios and configurations in a virtual environment prior to implementing changes to the physical asset. This can result in streamlined processes and a decrease in trial-and-error efforts. * Reduced downtime and maintenance Costs. By predicting and preventing issues before they occur, IDTs can help reduce unplanned downtime and maintenance costs. * Lifecycle management: IDTs cover the entire lifecycle of an asset, from design and development to operation and maintenance, and even decommissioning. The authors of Ref. [15] list the enabling technologies that turn the DT paradigm into a concrete opportunity for industries to undertake the digitization process fostered by the Industry 4.0 revolution.