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Digital twin

A digital twin is a virtual representation of a physical product or process, used to understand and predict the physical counterpart’s performance characteristics. Digital twins are used to simulate, predict and optimize the product and production system before investing in physical prototypes.

What is a digital twin?

A digital twin is a virtual representation or digital counterpart of a physical object, system or process. It is created using real-time data, simulation and modeling techniques to mirror the behavior, characteristics and performance of its physical counterpart. Digital twins are used across various industries, including manufacturing, healthcare, transportation and energy, to optimize performance, monitor operations and facilitate decision-making.

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Digital twin of new jet design.

Uncover the benefits

Digital twins offer a powerful framework for gaining insights, optimizing performance and driving innovation across various industries by bridging the gap between the physical and digital worlds.

Product lifecycle management

Comprehensive automated design

Machine automation engineering

Performance engineering

Low-code application development

E/E systems development

Discover key features of digital twins

Digital twins eliminate the need for physical prototypes, reduce development time and improve quality by combining multi-physics simulation, data analytics and machine learning to demonstrate the impact of design changes, usage scenarios, environmental conditions and other variables.

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Real-time data integration

Digital twins are continuously updated with real-time data from sensors, IoT devices, and other sources, providing an accurate representation of the physical asset or system at any given time.

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Simulation and modeling

Digital twins often incorporate simulation and modeling techniques to simulate the behavior and performance of the physical asset or system under different conditions. This allows for predictive analysis, optimization and scenario planning.

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Bi-directional communication

Digital twins enable bi-directional communication between the virtual model and its physical counterpart. This means that data and insights from the digital twin can inform decisions and actions in the physical world, and vice versa.

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Monitoring and control

Digital twins allow for real-time monitoring and control of the physical asset or system from a virtual environment. This enables remote monitoring, diagnostics and predictive maintenance to optimize performance and reduce downtime.

Examples of digital twins in the industry


Digital twins of manufacturing processes and equipment can optimize production schedules, predict equipment failures and improve overall efficiency.

Smart cities

Digital twins of urban infrastructure, such as transportation networks and utilities, can optimize traffic flow, manage energy consumption and enhance public services.


Digital twins of patient physiology and medical devices can support personalized treatment plans, monitor health metrics remotely and simulate surgical procedures.


Digital twins of power plants and renewable energy systems can optimize energy production, predict equipment failures and manage grid stability.

The Digital Twin in CAD and simulation software

Digital twin modeling can be part of both CAD (Computer-Aided Design) software and simulation software, depending on the specific functionalities and capabilities of the software in question.

CAD software

CAD software is primarily used for creating detailed 3D models of physical objects or systems. In the context of digital twins, CAD software can be used to create the virtual representation or geometry of the physical asset. This includes modeling the geometry, structure, components, and assemblies of the physical object. CAD software typically focuses on geometric representation and design intent, allowing engineers to create accurate virtual models of products or systems.

Simulation software

Simulation software, on the other hand, is used to simulate the behavior and performance of physical systems under various conditions. Simulation software can incorporate digital twin modeling by integrating real-time data, physics-based models, and simulation techniques to create a virtual representation of the physical asset. This includes simulating the dynamic behavior, interactions, and performance characteristics of the physical system. Simulation software focuses on analyzing and predicting the behavior of the system based on underlying physics principles.

In practice, digital twin modeling often involves a combination of CAD software and simulation software. CAD software is used to create the geometric representation of the physical asset, while simulation software is used to simulate the behavior and performance of the digital twin. The integration between CAD and simulation software allows engineers to create comprehensive digital twins that accurately represent the physical system and its dynamic behavior. Furthermore, some software platforms offer integrated solutions that combine CAD and simulation capabilities into a single platform, allowing users to seamlessly transition from design to analysis within the same environment. These integrated solutions enable engineers to create, simulate and optimize digital twins more efficiently and effectively.

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Digital Twin FAQs

The potential applications for a digital twin depend on what stage of the product lifecycle it models. Generally speaking, there are three types of digital twins: product, production, and performance. The combination and integration of the three digital twins as they evolve together is known as the digital thread. The term "thread" is used because it is woven into, and brings together data from, all stages of the product and production lifecycles.

  1. Product digital wins

    Product digital twins replicate physical products in a digital form. They are used in product design, testing, and simulation. Product digital twins help engineers and designers analyze how a product will perform under different conditions, enabling them to optimize its design and functionality before physical production begins.

  2. Process digital twins

    Process digital twins simulate and analyze the behavior of physical processes or systems. They are used to monitor, control, and optimize the operations of complex systems such as manufacturing plants, supply chains, and energy grids. Process digital twins enable organizations to visualize, simulate, and analyze processes in real time, facilitating better decision-making and performance optimization.

  3. System digital twins

    System digital twins replicate entire systems or ecosystems in a digital environment. They integrate multiple digital twins of products, processes, and other components to simulate the behavior of complex systems comprehensively. System digital twins are used to model and analyze large-scale systems such as smart cities, transportation networks, and industrial complexes.

Unlike traditional digital twins, which are primarily used for monitoring and analysis, executable digital twins are active, dynamic models that can respond to inputs, simulate scenarios, and make decisions autonomously or with human intervention. The executable digital twin (or xDT). In simple terms, the xDT is the digital twin on a chip. The xDT uses data from a (relatively) small number of sensors embedded in the physical product to perform real-time simulations using reduced-order models. From those small numbers of sensors, it can predict the physical state at any point on the object (even in places where it would be impossible to place sensors).

Real-time simulation and interaction

Executable digital twins (xDT) are capable of simulating the behavior and performance of the physical asset or system in real time. They can respond to inputs, simulate different operating conditions, and interact with external systems or users dynamically.

Autonomy and decision-making

Executable digital twins (xDT) can make decisions autonomously based on predefined rules, algorithms or machine learning models. They can analyze data, predict outcomes and take actions to optimize performance or respond to changing conditions.

Closed-loop control

Executable digital twins (xDT) often operate in a closed-loop control system, where real-time data from sensors and actuators are fed back into the virtual model to adjust parameters, optimize performance and maintain desired operating conditions.

Predictive analysis and optimization

Executable digital twins(xDT) use predictive analytics and optimization techniques to forecast future behavior, identify potential issues or opportunities and recommend actions to improve performance or mitigate risks.

Integration with IoT and AI technologies

Executable digital twins(xDT) leverage Internet of Things (IoT) sensors, connectivity and artificial intelligence (AI) algorithms to collect real-time data, analyze complex patterns and make informed decisions. They may also incorporate machine learning models for adaptive behavior and continuous improvement.

Dynamic adaptation and learning

Executable digital twins(xDT) are capable of learning from experience and adapting to changes in the environment or operating conditions over time. They can continuously update their models, parameters and strategies based on new data and feedback.

Executable digital twins find applications across various industries, including manufacturing, energy, transportation, healthcare and smart cities. They enable predictive maintenance, autonomous operation, optimization of processes and decision support in complex systems where real-time monitoring and control are critical. Overall, executable digital twins represent the next evolution in digital twin technology, offering enhanced capabilities for real-time simulation, decision-making and optimization of physical assets and systems. An executable digital twin is an advanced form of a digital twin that not only represents a virtual replica of a physical asset or system but also has the capability to execute, simulate and interact with the virtual model in real time.

Physics-based models

A physics-based executable digital twin relies on mathematical models that describe the physical behavior of the system being replicated. These models are typically based on fundamental principles of physics, such as mechanics, thermodynamics, fluid dynamics, electromagnetics, and so on. By solving the equations that govern these physical phenomena, the digital twin can simulate the behavior of the real-world system in a virtual environment.

Simulation of physical processes

The digital twin simulates the physical processes and interactions within the system using physics-based models. This allows it to predict how the system will behave under different operating conditions, inputs and scenarios.

Real-time simulation

An executable digital twin based on physics models can simulate the behavior of the physical system in real-time or near-real-time. This enables dynamic interaction and decision-making based on the current state of the system and its environment.

Closed-loop control

Physics-based executable digital twins often operate in a closed-loop control system, where real-time data from sensors and actuators are used to adjust the simulation parameters and control the behavior of the virtual model. This allows the digital twin to maintain desired operating conditions and optimize performance.

Validation and verification

Physics-based models used in executable digital twins must be validated and verified to ensure their accuracy and reliability. This involves comparing simulation results with real-world measurements and experimental data to confirm that the digital twin accurately represents the physical system.

While physics-based modeling is commonly used in executable digital twins, it's important to note that other modeling approaches, such as data-driven modeling, empirical models, or hybrid models combining physics and data-driven techniques, may also be employed depending on the specific requirements and constraints of the application.

Learn more

Digital twins are used across various industries, including manufacturing, healthcare, transportation and energy, to optimize performance, monitor operations and facilitate decision-making.

Digital manufacturing in industry

The production digital twin helps manufacturers create new business models, improve collaboration between teams and organizations, boost process, increase product and production quality and speed up time to market.

Digital twin in manufacturing

Enable manufacturing with a digital twin to connect real time intelligence to interconnected machines in the shop floor enabling them to orchestrate and execute the whole production in an efficient manner.

Unlock the power of the digital twin

This team used the Siemens Xcelerator portfolio to design, analyze and verify a disposable assembly that could be additively manufactured and securely connects two patients to one ventilator.