Digital Twin Technologies in Cyber-Physical Systems

Digital twin technologies provide a dynamic virtual counterpart to physical assets by seamlessly integrating high-fidelity models with real-time data streams. Within cyber-physical systems, these twins serve as closed-loop platforms that mirror, analyse and optimise performance across the lifecycle of machinery, infrastructure and processes. Key enabling technologies include interconnected sensors, edge and cloud computing, advanced data analytics and artificial intelligence, which together support continuous synchronisation between physical and virtual domains. Practical applications span predictive maintenance in manufacturing, performance optimisation in energy systems, autonomous control in transport and informed decision-making in smart cities. Despite demonstrable gains in efficiency, reliability and safety, challenges persist in achieving model fidelity, ensuring data interoperability and safeguarding against cyber threats. Emerging trends point towards standardised architectures, self-learning models and scalable frameworks to underpin the next generation of resilient and intelligent cyber-physical ecosystems.

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Digital Twin Technologies in Cyber-Physical Systems publication trend

The graph below shows the total number of articles in digital twin technologies in cyber-physical systems across all publications each year (not limited to Nature Index journals).

Technical terms

Digital twin: A digital replica of a physical asset or system that is continuously updated with real-time data to mirror its state and predict future behaviour.

Cyber-physical system: An assembly of interconnected computational and physical elements, where embedded software and networking coordinate monitoring and control.

Real-time synchronisation: The continuous exchange and processing of data between physical and virtual entities to ensure the digital twin accurately reflects current operating conditions.

Model fidelity: The degree of accuracy and detail with which a virtual model represents the physical counterpart, influencing prediction quality and decision-making reliability.

Predictive maintenance: A technique that uses analytics and model-based forecasts from digital twins to anticipate equipment failures and plan interventions before breakdowns occur.

References

1. Digital twin of electric vehicle battery systems: Comprehensive review of the use cases, requirements, and platforms. Renewable and Sustainable Energy Reviews (2023).
2. Characterising the Digital Twin: A systematic literature review. CIRP Journal of Manufacturing Science and Technology (2020).
3. Digital Twin: Enabling Technologies, Challenges and Open Research. IEEE Access (2020).
4. Leveraging Digital Twin Technology in Model-Based Systems Engineering. Systems (2019).