“A digital twin is a set of virtual information constructs that mimics the structure, context, and behavior of a natural, engineered, or social system (or system-of-systems), is dynamically updated with data from its physical twin, has a predictive capability, and informs decisions that realize value. The bidirectional interaction between the virtual and the physical is central to the digital twin.”

The Committee on Foundational Research Gaps and Future Directions for Digital Twins adopts a modified definition of a digital twin from that initially formulated by the AIAA Digital Engineering Integration Committee in 2020. According to this refined interpretation, a digital twin consists of virtual informational replicas that accurately reflect systems’ structure, context, and dynamics, whether natural, engineered, or social, and systems composed of interlinked subsystems.

This digital representation is continually synchronized with its physical counterpart through data updates, encompasses the ability to forecast future states, and enables decision-making processes that generate value. The essence of a digital twin lies in its two-way communicative link, allowing for continuous data exchange from the physical entity to its digital counterpart and vice versa, facilitating informed decision-making processes, whether automated or involving human intervention.

The committee’s elaboration further broadens the scope of what constitutes digital twins’ physical counterparts, spanning artificial constructs, organic life forms, and societal constructs. It underlines the importance of predictive capabilities in digital twins, stressing the role of enabling proactive decision-making based on insights extended beyond current data. In summary, this enhanced definition strongly emphasizes reciprocal interaction as a pivotal feature.


“Across multiple domains of science, engineering, and medicine, excitement is growing about the potential of digital twins to transform scientific research, industrial practices, and many aspects of daily life. A digital twin couples computational models with a physical counterpart to create a system that is dynamically updated through bidirectional data flows as conditions change. Going beyond traditional simulation and modeling, digital twins could enable improved medical decision-making at the individual patient level, predictions of future weather and climate conditions over longer timescales, and safer, more efficient engineering processes. However, many challenges remain before these applications can be realized.”

“This report identifies the foundational research and resources needed to support the development of digital twin technologies. The report presents critical future research priorities and an interdisciplinary research agenda for the field, including how federal agencies and researchers across domains can best collaborate.”

Contributor(s): National Academy of Engineering; National Academies of Sciences, Engineering, and Medicine; Division on Engineering and Physical Sciences; Division on Earth and Life Studies; Board on Mathematical Sciences and Analytics; Committee on Applied and Theoretical Statistics; Computer Science and Telecommunications Board; Board on Life Sciences; Board on Atmospheric Sciences and Climate; Committee on Foundational Research Gaps and Future Directions for Digital Twins.

(Source: National Academies of Sciences, Engineering, and Medicine. 2024. Foundational Research Gaps and Future Directions for Digital Twins. Washington, DC: The National Academies Press. https://doi.org/10.17226/26894.)

© 2024 National Academy of Sciences. All rights reserved.

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