This course introduces students to the concept of digital twins, virtual replicas of physical systems used for monitoring, simulation, and predictive analysis. Beginning with a foundational overview of digital twins, we explore their integration with the Internet of Things (IoT), where real-time data is harnessed to mirror and optimize physical assets and processes. The course then categorizes various types of digital twins (such as component, system, and process twins) illustrating their distinct applications and characteristics.
Focusing on real-world applications, we examine technical domains like manufacturing and smart infrastructure, as well as healthcare applications for personalized medicine and patient monitoring. A comparison between the digital twin of a human (focused on mimicking physiological aspects) and the concept of a digital human (encompassing cognitive and behavioural modelling) further differentiates their unique roles and functionalities. The course also covers digital twin computing, a field dedicated to advancing the computational power and resources required for digital twin ecosystems.
In the development section, students learn the stages and technologies involved in creating digital twins, including data acquisition, model building, and validation processes. The course concludes by summarizing the transformative potential of digital twins across industries and envisioning their future impact on complex, data-driven environments.
The knowledge and competencies students will gain after studying this course include: foundational understanding of digital twins and their integration with IoT; technical and practical applications; understanding of differences and similarities between digital twins and digital human representations; digital twin computing and computational resources; design and development of digital twins; vision and future of digital twins.
- Grieves M., Vickers J. Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. Transdisciplinary perspectives on complex systems. Springer, 2017, pp. 85–113.
- Grieves M. Virtually Intelligent Product Systems: Digital and Physical Twins. Complex Systems Engineering: Theory and Practice. American Institute of Aeronautics and Astronautics, 2019, pp. 175–200.
- Glaessgen, E. H., Stargel, D. The digital twin paradigm for future NASA and US air force vehicles. AAIA 53rd Structures, Structural Dynamics, and Materials Conference, Honolulu, Hawaii. 2012.
- Mostaq Hossain S. M., Kumar Saha S., Banik S., Banik T. A New Era of Mobility: Exploring Digital Twin Applications in Autonomous Vehicular Systems. 2023 IEEE World AI IoT Congress (AIIoT), Seattle, WA, USA, 2023, pp. 0493-0499.
- Madni A. et al. Leveraging Digital Twin Technology in Model-Based Systems Engineering. Systems, 2019. No 7, pp. 1–13.