## 1 Introduction
The paper focuses on the links between DT and classical computational mechanics and does not aim to be a thorough review of the vast DT literature and its applications in other areas. Digital Twins as the Extension of the Parametric Simulation, Parameter Identification, and Structural Health Monitoring  
A digital twin can be defined as a representation of a physical model in a digital form that can be adapted to provide reliable predictions of the response of the physical model in a changing environment. A physical model can be a building, electromechanical equipment, infrastructures, etc. The data collected concerning a structure's design and operation are significant for creating a DT. According to the Gemini Principles, the digital twin is the digital representation of an element, process, or system in its physical environment [2]. The digital twin (DT) has been advertised as a standard model that incorporates high-accuracy simulations of a structure, operational parameters (structural and SHM), and maintenance data as well as historical operational data to capture the current state of the physical twin, to conduct lifecycle management, and to estimate the safety, reliability, and maintenance of the structure. It can be considered an extension of classical 3D CAD models into models of higher dimensions, for example, a 5D model if time and measurements were added. DT was first introduced in the aerospace industry and currently appears in several industries. A digital twin can be created, regardless of size, from individual elements (components, pipelines, etc.) to complex processes and environments (plants, production lines, wind farms, hydroelectric parks, etc.). The complexity depends on the information capability of the infrastructure. DT is part of modern technological and scientific developments. Low-cost sensors are being developed to support IoT, big data are being generated and transferred via modern wireless networks (5G), artificial intelligence and deep learning promise to perform complex data processing tasks even on-site (edge computing), and multi-physics computational techniques can reproduce the behavior of the physical model with high accuracy. This integrated system with physical and digital properties is called Industry 4.0. The creation of a digital twin requires several elements, such as:  
1. Sensors that record and collect data for the operational behaviors of the components and processes (vibrations, temperature, pressure, etc. ), along with their operating environments (air temperature, humidity, etc.). 2. Communication networks providing secure, wireless, and reliable data transfer from devices to the digital world. 3. Local preprocessing, in the case of the limited capacity of communication networks. 4. A digital platform that works as a modern database. It can collect and store enormous data from a plant, and by combining these data, information can be generated for data-driven decision-making techniques using, among others, advanced artificial intelligence/machine-learning algorithms. The most common applications of a digital twin are carried out for the following two reasons:  
1. Maintenance prediction: By gaining a holistic view of the operation and the performance of the physical model, the user can detect the deviation in its operations. The maintenance and the replacement of spare parts can be pre-scheduled to minimize time and reduce failures. For manufacturers, predictive maintenance using digital twins can provide new service-based revenue while helping to improve product reliability. Examples are discussed in [17; 23; 27; 28]. 2. Process design and optimization: A digital footprint that collects sensor data from a production line, as well as data from enterprise resource planning (ERP) systems, can thoroughly analyze key performance indicators (KPIs), such as production rates and data quantities that are practically \"useless.\" This helps diagnose the cause of any inefficiencies and performance losses, thereby optimizing performance and reducing wasteful information.