# Design Technology and AI-Based Decision Making Model for Digital Twin Engineering
## 2 Literature Review
Data-driven modeling can be applied at the operational stage of an object's (product) life cycle, when it is possible to obtain feedback from it. Mathematical models based on physical processes are more promising for problems with a situational analysis and for decision making under \"what-if?\" condition analysis. In addition, hybrid models can be used in non-recurring situations where there are not enough data to apply statistical methods. On the basis of the additional information obtained during the operation stage, the level of adequacy of the hybrid model increases, that is, the DT is trained and makes it possible to further predict the level of possible deviations from normal modes and damage to equipment, or evaluate its residual life [10]. It should be noted that at different stages of DT design, there is a different amount of data about object. At the development stage, there are no data from a real object, since there is no physical product (product) itself, and data about an object can only be obtained on the basis of modeling physical processes that determine the creation and functioning of a future product. As product data accumulate, the latter can increasingly be used to build analytical models. A mathematical model based on physical processes can be created before the stage of creating a real object, and can predict its behavior over a wide range when the boundary conditions of the numerical simulation problem change. A DT with a high level of adequacy should combine both physical process models and data-driven models. Smart digital twins with intelligent controls should combine both of these approaches, enhancing the benefits of each of them. The use and scope of digital twins is very wide. They are used not only in heavy engineering [14; 15; 16], in the automotive industry [17] and building [18], in the field of nuclear energy [19], in the aerospace [20; 21] and oil and gas industries [22], in architectural design and creating smart cities [23], and agriculture, but they are also used to improve operational efficiency in the production of consumer goods [5], the accuracy of diagnostics and decision making in healthcare [24], to attract customers and customize services in the financial sector [25] and in retail [26], for organizing logistics processes and supply chains [27], and in regional and municipal management [28; 29]. Demand for and the range of application of DTs is expanding. Since their development and implementation are based on a number of rapidly developing technologies, the development of digital storage directly depends on the growth of the capabilities of these technologies. This is due to:  
* The development of quantum technologies and the increase in the speed of computing systems [30; 31]. If general-purpose quantum computing is ever realized, there would be a qualitative leap in the speed of hardware systems. This will make it possible to perform numerical analyses based on already existing (and more complex) models in a time acceptable for the operational interaction of a physical object and its digital copy. Today, companies are working to develop and use quantum algorithms to model complex physical processes. The transition to such technologies will speed up the solution of problems based on numerical modeling, providing for the required accuracy of algorithms under the conditions of the available computing resources (problems of multi-parameter optimization, etc. );
* The development of 5G and 6G technologies [32; 33; 34]. These technologies have higher throughput, lower latency, and lower battery consumption of IoT sensors. This provides an increase in the speed of signal transmissions between the physical object and its DT. The use of 5G networks will make it possible to construct virtual reality services as part of digital twins and make available the virtual verification and validation of finished products.