Introduction and Background

The digital twin has theoretical foundations in information science, production engineering, data science and computer science. Tao et al identify four key aspects of digital twin research: modeling and simulation, data fusion, interaction and collaboration and service. Modeling plays an important role in the development of a digital twin. Models can be broadly classified as real models, physics-based models and empirical models. Modern systems are equipped with a large number of sensors which routinely collect measurements. Estimation methods play an important role in performing this process. A widely used estimation method is the Kalman filter. Digitals twins need to be applied in real-world applications to become useful. There have been several cases where digital twin technology has been applied across different industries. Digital twins are attractive for maintenance applications and represent one of the main areas of their application. Digital twin concept has been applied for the management of smart cities.