Researchers claim to have created a new AI-assisted digital twin model to change and operate the real machine in real-time. The study was published in the journal IEEE Xplore.
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The study gives digital representations of actual machines—such as robots, drones, or even driverless cars—a new dimension.
Digital twins are precise duplicates of real-world objects. They are continuously updated with real-time data and are compared to digital twins of real devices found in video games.
Digital twins allow scientists and engineers to test and monitor machinery without touching the actual system, making industries more intelligent and productive.
However, the majority of digital twins nowadays are merely observers. They can forecast and analyze potential outcomes, but they cannot take independent action.
This is where the authors' model is useful. In contrast to conventional digital twins, their Intelligent Acting Digital Twins (IADT) do more than only observe.
Imagine a drone chasing an enemy aircraft. A traditional digital twin would simulate different scenarios and suggest possible moves. But with IADT, the digital twin can actually autonomously control the drone, learning from human pilots and eventually making its own decisions.
Dr. Ahcene Bounceur, Study Lead Author and Associate Professor, Computing and Informatics, University of Sharjah, UAE
According to Dr. Bounceur, an Associate Professor at the University of Sharjah's College of Computing and Informatics in the United Arab Emirates (UAE), IADT can have broad applications in the industrial sector and many other fields that directly affect human life.
Bridging the gap between virtual and physical, and by learning from humans and acting independently, this (IADT) could be useful in many fields—healthcare, smart cities, self-driving cars, and improving real-time responses even in the event of a disaster.
Dr. Ahcene Bounceur, Study Lead Author and Associate Professor, Computing and Informatics, University of Sharjah, UAE
Dr. Bounceur is certain that the model has “significant practical implications across multiple industries and believes that its introduction would open the door to real-world applications where digital twins can go beyond just monitoring and simulation—they can now act, adapt, and autonomously control real-world systems in real time.”
Furthermore, Dr. Bounceur concludes that the approach may be used practically in important fields like autonomous vehicles and robotics, healthcare and medical technology, smart cities and infrastructure management, defense, and aerospace, revolutionizing the way AI is used in digital twins worldwide.
A true digital twin should not just mirror the real world—it should interact with it, adapt to it, and even control it. That’s what we have achieved with IADT. The future isn’t just automation, it’s intelligence. We are building systems that don’t just follow commands, but understand their environment, make decisions, and act in real time.
Dr. Mostefa Kara, Study Co-Author, King Fahad University of Petroleum and Minerals
The researchers, in their research, declared their IADT to have “a groundbreaking capability” with Dr. Kara emphasizing that the model “integrates AI with digital twins and moves towards a world where machines don’t just assist humans, but they collaborate, adapt, and act on their own. For too long, digital twins have been passive observers—monitoring and predicting but never acting. With IADT, we’ve changed that. Now, digital twins can think, learn, and take action in real time.”
Of their IADT concept, the authors write that it “represents a significant advancement in leveraging digital twin technology. IADT enables individuals to utilize a digital twin to control real-world systems, ultimately aiming for complete autonomy where the digital twin can autonomously manage the real system, eliminating the need for direct human intervention. We have delineated two distinct types of digital twins: one focused solely on a device’s behavior and another encompassing the behavior of the entire system.”
The authors assert that through real-world applications utilizing the CupCarbon platform, they have confirmed the viability of their approach.
“These implementations demonstrate how the IADT integrates virtual and physical components to create a unified and effective framework, offering a significant advancement in the application of digital twin technology across various domains,” noted the authors.
The research touts the model as a bold step towards fully autonomous systems. “By combining machine learning, AI, and digital twins, we move toward a future where machines can act and adapt without waiting for human input. This is essential for emergency response, automation, and high-risk industries where quick, intelligent actions are needed,” said the authors.
In their conclusion, the authors reiterate that their “proposed architecture for ADT not only enables the integration of new features and behaviors into real systems but also offers a new design methodology for circuit designers venturing into digital twin applications. Through this work, we envision a future where digital twins play a pivotal role in achieving autonomy and optimization across various domains, revolutionizing the way we interact with and control real-world systems.”
Journal Reference:
Bounceur, A., et al. (2025) Intelligent Acting Digital Twins (IADT). IEEE Access. doi.org/10.1109/ACCESS.2025.3532545