Contents I. Introduction
Digital twins (DTs) are a form of technology that connects the physical and virtual worlds [1]. This technology allows for a smooth transition between the virtual and physical worlds by removing the constraints caused by physical surroundings [2]. It goes by several names: digital characters and digital echoes. Industry 4.0 relies on DT technology to bridge the gap between the digital and physical worlds [3]. It includes creating a precise digital representation of a physical system or item, such as machines, buildings, networks, and cities, and continuously updating it with real-time data from sensors to ensure it accurately represents the physical object’s status and condition. According to Kenett et al. [4], research, DTs have become increasingly important, with applications spanning various industries, including manufacturing, construction, energy, healthcare, and aerospace. The Internet of Things (IoT), big data analytics, and cloud computing are some of the most recent technological developments that have opened up exciting possibilities for the improvement and advancement of DT. Its real-world applications ranging from space exploration and aerospace systems analysis to predictive maintenance and production efficiency in the industrial sector [5]. Moreover, DTs have gained prominence in addressing contemporary challenges, such as disease transmission modeling, healthcare facility management, urban planning, environmental monitoring, and sustainability. Integration with AI and big data analytics has enhanced prediction capabilities across diverse sectors. Numerous possible applications for DTs have been widened by developments in associated technologies, including augmented reality (AR), deep learning (DL), virtual reality (VR), and machine learning (ML) [6]. By allowing users to interact with DT overlaid onto the real world and virtual environments, VR and AR have revolutionized the ability to visualize and simulate. The optimized operations, anomalies are discovered, and predictive analytics amidst the deluge of data produced by DT, ML, and DL methods have been indispensable. Embracing these technologies, the U.S., Germany, Japan, China, and South Korea are at the forefront of the DT. These nations have made significant financial commitments to research and development, creating conditions that promote innovation and cooperation among academic institutions, corporations, and governments. Globally, countries have implemented supportive policies and initiatives to incorporate ML, VR, AR, and DL into their DT strategies across various industries, including manufacturing, healthcare, and aerospace [7]. This integration is expected to enhance efficiency, productivity, and competitiveness. The IoT, AI, and data analytics have all contributed to the rapid rise in popularity and widespread adoption of DT technology across diverse industries. DT serves various purposes in numerous fields. For instance, in the manufacturing sector, it is improving and standardizing production processes. According to Xiao et al. [8], it is beneficial for developing personalized medical treatments in the healthcare industry and for enhancing urban planning and sustainability in smart cities. DT is projected to have a significant influence on the future of organizations and technological advancements as technology continues to improve.
