Digital Twins are transforming the way we connect the physical and digital worlds, enabling real-time simulation, prediction, and optimization across industries. In this interview, we speak with researcher Seyed Mojtaba Hosseini Bamakan, whose work at ECON MUNI within the MSCA OP JAK Fellowship program focuses on advancing Digital Twin technologies and addressing their key challenges in complex, data-driven environments.
What initially drew me in was the idea that Digital Twins can directly connect the physical world with the digital one. At its core, a Digital Twin is a virtual replica of a real object, system, or process that behaves like its physical counterpart. This is possible because it continuously receives real-time data from sensors and other sources.
That real-time connection is what makes the concept so powerful. It allows us not only to observe systems, but to simulate, monitor, and predict what might happen without taking risks in the real world.
Interestingly, the idea itself is not new. It dates to the 1970 s, when NASA used early forms of Digital Twins to support space missions and improve maintenance planning. What has changed is the technological environment. With advances in IoT (internet of Things – interconnected devices that collect and exchange data), big data (large-scale data processing and analysis), cloud computing (on-demand data storage and computing over the internet), and AI, Digital Twins have become a key part of modern industry and research. Today, they play an important role in fields such as manufacturing, healthcare, logistics, and smart city development.
Their biggest advantage is that they allow organizations to test and evaluate decisions in a digital environment before applying them. This leads to smarter decisions, fewer errors, and lower expenses.
One of the key benefits of Digital Twins is real-time monitoring. Because the digital model is continuously updated with live data, it provides immediate and accurate insight into system performance, enabling organizations to detect issues at an early stage, sometimes even before they occur which significantly reduces downtime. In addition, Digital Twins support predictive maintenance, allowing companies to anticipate when maintenance will be required instead of reacting to unexpected failures; this not only lowers repair costs but also extends the overall lifespan of equipment. They also contribute to more effective resource management, as the insights they generate into energy consumption, material usage, and workforce allocation help organizations minimize waste and operate in a more efficient and sustainable way.
It is a common misconception; while simulations and Digital Twins are related, they are fundamentally different. A simulation is typically a static model designed to analyse how a system might behave under specific conditions, usually run for a particular scenario without continuous updates. In contrast, a Digital Twin is dynamic and continuously connected to real-world data, evolving alongside the system it represents. This allows it to monitor performance in real time, detect anomalies, and predict future behaviour. In simple terms, simulations answer the question “what could happen?”, whereas Digital Twins answer, “what is happening now and what will happen next?”, making them far more powerful for long-term optimization and decision-making.
There are several significant challenges in developing complex Digital Twins. The first is data management, as Digital Twins generate enormous amounts of real-time data from multiple sources, requiring advanced infrastructure and powerful processing capabilities. Another major challenge is protecting commercial information and intellectual property, since these projects often involve sensitive data, proprietary algorithms, and valuable industrial knowledge that must be securely managed. Cost is also a key factor, as building a sophisticated Digital Twin requires substantial investment in both technology and expertise, while demonstrating a clear return on investment can be difficult in the early stages. Finally, collaboration remains complex, because Digital Twin projects typically involve multiple stakeholders, and without effective systems for data sharing, access control, and coordination, the process can easily become inefficient and fragmented
Web3 technologies (a decentralized internet based on blockchain and user ownership of data) help solve many of these issues by making systems more secure, transparent, and collaborative. Decentralized storage systems like IPFS, Filecoin, and Arweave allow data to be stored without a central point of failure, improving reliability and availability. Blockchain ensures that data is tamper-proof and verifiable, which increases trust and security.
For intellectual property protection, we use IP-NFTs, which link digital assets such as models or algorithms to clear ownership records on the blockchain, allowing transparent tracking of creation and usage rights.
Given the high development costs of Digital Twins, NFTs can support fundraising by representing digital assets, such as model snapshots or usage rights, in a tokenized and transparent manner.
For collaboration and governance, we use DAOs (Decentralized Autonomous Organizations), which enable shared decision-making through smart contracts and ensure transparent and coordinated cooperation between participants.
It should be noted that the challenges discussed above are highly simplified. Addressing the challenges of Digital Twins in complex real-world environments requires a multi-layered framework that considers advanced technologies, integrated communication, and extensive system design.
The strongest potential of Digital Twins lies in areas where real-time data and complex decision-making intersect. Smart manufacturing is a clear example, as Digital Twins can predict equipment failures and optimize production processes. Healthcare is another promising field, especially with virtual patient models that can support more personalized treatment. Smart cities also represent a major opportunity, since Digital Twins can simulate traffic, energy consumption, and environmental changes to help policymakers make better decisions. At the same time, sectors such as energy and logistics are expected to increasingly rely on Digital Twins to improve efficiency, sustainability, and overall resilience.
Seyed Mojtaba Hosseini Bamakan, Ph.D., is a Senior Researcher and former Associate Professor recognized globally for his scientific impact, including being ranked among the Top 2% Most Cited Scientists Worldwide (Stanford University Ranking, 2023–2025). With an H‑index of 29 and more than 4100 citations, he conducts research in AI/ML, blockchain/Web3 technologies, decentralized cyber‑physical‑social systems, digital twins, and interpretable and privacy‑preserving learning models. His work is supported by major international grants such as the MSCA OP JAK Fellowship and CAS‑PIFI awards, and strengthened through broad international collaborations across Europe, China, and the Middle East.
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