Digital Twins

Digital Twins, sophisticated digital replicas of physical entities, have been gaining significant attention, especially after NASA's endorsement, and are poised to revolutionize numerous fields, such as medicine and construction. These advanced models offer dynamic, real-time simulations, leveraging enabling technologies, such as artificial intelligence, machine learning, IoT, cloud computing, and Big Data analytics to enhance their functionality and applicability. In the medical field, Digital Twins facilitate personalized treatment plans and predictive maintenance of medical equipment by simulating human organs with precision. In construction, they enable efficient building design and urban planning, optimizing resource use, and reducing costs through predictive maintenance. Startups are innovatively employing Digital Twins in various sectors, from smart cities—where they optimize traffic flow, energy consumption, and waste management—to industrial machinery, ensuring predictive maintenance and minimizing downtime. This survey delves into the diverse use cases, market potential, and challenges of Digital Twins, such as data security and interoperability, while emphasizing their transformative impact on industries. The future prospects are promising, with continuous advancements in AI, ML, IoT, and cloud computing driving further expansion and application of Digital Twin technologies.

Digital Twins (DTs) are revolutionizing supply chain operations by creating dynamic digital replicas of physical assets, processes, and systems. This paper explores the integration of Artificial Intelligence (AI) with Digital Twins to bridge the physical and digital worlds in supply chain management. By leveraging AI, Digital Twins can analyze real-time data, predict future events, and optimize decision-making processes. This synergy enhances operational efficiency, reduces costs, and improves responsiveness to disruptions. We delve into the architecture of AI-driven Digital Twins, highlighting their components, data flow, and interaction mechanisms. Case studies across different industries demonstrate the practical applications and benefits of this technology. The discussion includes challenges such as data privacy, integration complexity, and the need for standardized protocols. Future research directions focus on advancing AI algorithms for better predictive capabilities and creating more robust, scalable Digital Twin frameworks. This paper underscores the transformative potential of AI-enhanced Digital Twins in creating agile, resilient, and intelligent supply chains.

Digital Twin (DT) technology, when combined with Artificial Intelligence (AI), has emerged as a transformative approach for predictive maintenance in modern industries. This paper synthesizes secondary evidence from academic literature, industry reports, and international standards (2018–2025) to examine how AI-enhanced digital twins are enabling predictive maintenance across manufacturing, energy, and transportation. Digital twins replicate physical assets virtually, while AI models analyze real-time sensor data to detect anomalies, forecast failures, and optimize maintenance schedules. Using structured analysis of ISO standards, Gartner, McKinsey, and IEEE literature, we map how digital twins integrate with IoT data streams, machine learning models, and maintenance workflows. The study identifies key enabling technologies (IoT, cloud computing, ML/DL), common architectural layers, and documented benefits, such as reduction in unplanned downtime (30–50%) and improved asset life cycles. A conceptual flowchart illustrates the AI–DT predictive maintenance loop, and a table compares sectoral adoption patterns. Findings highlight the convergence between AI analytics and DT simulations, enabling data-driven, condition-based maintenance strategies. Challenges remain in data interoperability, model updating, and cybersecurity. Future work should focus on standardized frameworks and cost-benefit models for broader adoption.

Digital twins are virtual models of physical artefacts that may or may not be synchronously connected, and that can be used to simulate their behavior. They are widely used in several domains such as manufacturing and automotive to enable achieving specific quality goals. In the health domain, so-called digital patient twins have been understood as virtual models of patients generated from population data and/or patient data, including, for example, real-time feedback from wearables. Along with the growing impact of data science technologies like artificial intelligence, novel health data ecosystems centered around digital patient twins could be developed. This paves the way for improved health monitoring and facilitation of personalized therapeutics based on management, analysis, and interpretation of medical data via digital patient twins. The utility and feasibility of digital patient twins in routine medical processes are still limited, despite practical endeavors to create digital twins of physiological functions, single organs, or holistic models. Moreover, reliable simulations for the prediction of individual drug responses are still missing. However, these simulations would be one important milestone for truly personalized therapeutics. Another prerequisite for this would be individualized pharmaceutical manufacturing with subsequent obstacles, such as low automation, scalability, and therefore high costs. Additionally, regulatory challenges must be met thus calling for more digitalization in this area. Therefore, this narrative mini-review provides a discussion on the potentials and limitations of digital patient twins, focusing on their potential bridging function for personalized therapeutics and an individualized pharmaceutical manufacturing while also looking at the regulatory impacts.

• Examined various battery types, BMS architectures, and modelling techniques. • Explored deterministic, non-deterministic, and hybrid SOC estimation methods. • Discussed SOH estimation and RUL prediction using various methods. • Exploration of digital twin technologies and battery digital twins (BDTs) for enhancing BMS functionality. • Discussion of future research directions in battery state estimation. Accurate state estimation is fundamental for the safety, reliability, and performance of electric vehicle (EV) batteries. This review highlights the critical need for precise evaluation of key battery states such as state of charge (SOC), state of health (SOH), and remaining useful life (RUL). It surveys the existing modelling and estimation techniques used in lithium-ion battery (LIB) systems. It explores parameter identification methods and the challenges associated with real-time monitoring and predictive diagnostics in dynamic EV environments. The paper further investigates the transformative role of digital twin (DT) technologies in addressing these challenges. By integrating battery digital twins technologies(BDTs): Internet of Things (IoT), cloud computing, artificial intelligence (AI), and extended reality (XR), it offers a virtual replica of the battery system. That enables continuous monitoring, predictive maintenance, and performance optimization. The review delves into the architecture, functions, challenges and future perspectives of battery digital twins (BDTs). And it emphasizes their potential to enhance traditional battery management systems (BMS) through intelligent, adaptive control. Through comparative analysis and identification of research gaps, this review provides a roadmap for future advancements in state estimation and digital twin integration.

Cities worldwide are rapidly integrating urban management information technology (IT) systems to address increasing urban challenges effectively. This paper examines the evolution of these systems, from the Ubiquitous City (U-City) to contemporary Smart Cities, and explores emerging technologies like the Metaverse and Digital Twin. Despite their growing relevance, there is ambiguity in their conceptual definitions and how they are applied interchangeably in urban management contexts. This study aims to clarify these technologies by analyzing U-City, Smart City, Metaverse, and Digital Twin within an Analysis Framework of Urban Management IT Systems. Our analysis reveals distinct roles and interconnections among these technologies. U-City initiated the development of essential IT infrastructure, laying the groundwork for subsequent systems. Smart City technology, leveraging the infrastructure provided by U-City, addresses real urban issues but lacks predictive capabilities for future challenges. In contrast, the Metaverse facilitates the simulation of urban changes and planning in a virtual environment, influencing real-world city development. Digital Twin technology enhances the operational efficiency of U-City and Smart City by simulating real-world data and optimizing urban management processes. The study highlights that while U-City and Smart City function in the tangible realm, Metaverse and Digital Twin operate in the virtual domain, yet they are closely integrated. Metaverse assists in urban planning through simulations that inform real-world Smart City designs, while Digital Twin optimizes real-time city operations and informs Metaverse simulations for future planning. The findings underscore the importance of understanding these technologies’ unique contributions and their interplay. Although current urban management IT systems have limitations, especially in technology classification and data accessibility, this research paves the way for future investigations. Further research should focus on refining the definitions and applications of Metaverse and Digital Twin, clarifying their relationships with Smart City and U-City, and addressing security and privacy concerns associated with these technologies.

Biopharma Is Going Digital … Bit by Bit

In the last years, there has been a growing interest in the emerging concept of digital twins (DTs) among software engineers and researchers. DTs not only represent a promising paradigm to improve product quality and optimize production processes, but they also may help enhance the predictability and resilience of cyber-physical systems operating in critical contexts. In this work, we investigate the adoption of DTs in the railway sector, focusing in particular on the role of artificial intelligence (AI) technologies as key enablers for building added-value services and applications related to smart decision-making. In this paper, in particular, we address predictive maintenance which represents one of the most promising services benefiting from the combination of DT and AI. To cope with the lack of mature DT development methodologies and standardized frameworks, we detail a workflow for DT design and development specifically tailored to a predictive maintenance scenario and propose a high-level architecture for AI-enabled DTs supporting such workflow.

Predictive maintenance (PdM) is one of the enabling technologies of smart manufacturing in which artificial intelligence (AI) is used to predict equipment troubles, or even better anticipate the failure of equipment, based on sensor data, machine learning algorithms, and digital twins. The traditional techniques of maintenance such as the reactive maintenance technique and the preventive maintenance technique will most probably introduce undue downtimes, wastages or redundancy. In comparison, AI-oriented predictive maintenance is a combination of real-time data analytics, industrial internet (IIoT), and existing deep learning technology will allow equipment to operate 24/7, minimize the risk of operations, and make decisions. It is a review article on the use of AI-based predictive maintenance in smart manufacturing. It talks about the establishment of PdM, how AI can develop real-time faults and use machine learning, deep learning and deep reinforcement learning procedures. Providing the advantages of PdM systems, the list below states with comment of automatic saving in case of downtime, of low cost and sustainability of aerospace case, case of automotive and case of energy. The issue of the heterogeneity of the data, non-standardization, threat to cybersecurity, and explainable AI is solved. Predictive maintenance and its combination with Industry 4.0, such as digital twins, edge computing, and blockchain, is another item on the list of the ways to make manufacturing systems more autonomous and resilient. The paper ends with a definition of future directions that include hybrid AI model, federated learning of collaborative PdM, and explainable AI model of trust and adoption. Smart manufacturing ecosystems can enable all this by adding intelligence to maintenance to move to more sustainable, reliable, and adaptable operations as defined in Industry 5.0.

In modern medicine, novel technologies play a key role in improving the diagnosis, treatment, and management of patient health. One of the most promising innovations is Digital Twin technology, which enables the creation of virtual models of real-world objects, including human organs. This technology opens new possibilities for personalised medicine, disease prediction, and the optimisation of medical processes. Digital Twins are already actively used in various industries, such as manufacturing, aviation, and urban planning, but their potential in medicine is only beginning to unfold. They allow for creating detailed models of an individual patient’s body, contributing to more accurate diagnostics, simulation of treatment responses, and enhanced efficiency of medical interventions. This article explores the key aspects of Digital Twin medical applications, their advantages, challenges, and development prospects. Special attention is given to the technical foundations of this technology, its use in personalised medicine, health monitoring, disease prediction, and the optimisation of healthcare facilities. For the practical part, the study shows how to create a Digital Twin of a heart based on real patient data. Additionally, the study investigates a model of the heart’s Digital Twin, demonstrates its use, and illustrates how to replicate heart functionality using the Digital Twin. The study describes how a part of a Digital Twin was created using the suggested architecture and outlines how to use and extend this functionality. More than that, the study shows how to integrate artificial intelligence into the Digital Twin architecture and how artificial intelligence algorithms help create Digital Twins. The research shows how to build the Digital Twin using machine learning and artificial intelligence technologies, and how to choose the mathematical model for the Digital Twin. To conclude, the study defines further work, problems, and difficulties in creating the Digital Twin of the heart. The study shows the perspectives on using Digital Twins, their practicality, and real-life cases where Digital Twins can save lives. Also, the study defines the next steps for the Digital Twin creation, testing, implementation, and usage in the real-life healthcare industry.

The term Digital Twin, referring to the digital representation of an indistinguishable physical system or process in the real world, is a relatively new technology that has gained attention in recent years due to its potential to improve efficiency and decision-making in various industries. The concept behind Digital Twins is to create a virtual replica of a physical asset or process, allowing for real-time monitoring, data analysis, and simulation of different scenarios. This technology can be applied to various industries such as manufacturing, construction, and oil and gas. In the oil and gas industry, Digital Twins can provide several benefits. For example, they can improve operational efficiency by providing real-time data on the performance and condition of assets, allowing for better decision-making and reducing downtime. Additionally, Digital Twins can also help with asset management by providing detailed information on the performance and maintenance requirements of assets, which can be used to optimize their operation and extend their service life. Furthermore, Digital Twins can also be used to simulate the performance of new assets before they are built, reducing the risk of errors and increasing the chances of success. One of the key aspects of Digital Twins is that they can be updated in real-time, allowing for continuous optimization of the system. This is achieved by integrating various technologies such as sensors, IoT devices, and machine learning algorithms, which provide data on the physical asset and its environment. The data is then processed and analyzed, providing insights that can be used to improve the performance of the asset.

The aircrafts were top of the places that disrupted the seamless connectivity requirement of 5G and beyond. The Aeronautical Ad-hoc Networks (AANETs) take the attention of both industry and academia to satisfy this connectivity requirement with the low cost, easy deployment, and continuous coverage features. On the other hand, the ultra-dynamic characteristics of AANET with unstructured topology make its environment hard-to-follow. Here, Artificial Intelligence (AI)-based methodologies have an essential role in handling the management complexity of this hard-to-follow environment. However, these methodologies increase the computational complexity of aircraft due to the continuous update, convergence time, and scalability issues. At that point, we propose the utilization of the Digital Twin (DT) technology to handle the management complexity of AANET while solving the main issues of AI-based methodologies on it. The DT can virtually replicate the physical AANET components through closed-loop feedback in real-time. Therefore, this work introduces the utilization of DT technology for the AANET orchestration and, accordingly, proposes a DT-enabled AANET (DT-AANET) topology management framework. This framework consists of the Physical AANET Twin and Controller, including Digital AANET Twin with Operational Module. Here, the Digital AANET Twin virtually represents the physical environment while the operational module executes the AI-based computations on them through unsupervised learning-based training or supervised learning-based prediction. Finally, we present a case study based on Learning Vector Quantization (LVQ) to show the usability of the proposed framework and support this through evaluation results.

Artificial intelligence (AI) is increasingly vital in modern mineral processing, where it addresses critical challenges such as falling ore grades, rising energy costs, and the demand for sustainable operations. Despite notable progress, most existing studies focus on individual applications like ore sorting or predictive maintenance and lack a holistic view of AI-enabled mineral processing systems. This review aims to bridge that gap by examining how AI tools can be integrated into a unified workflow that spans ore characterization, sorting, and real-time process optimization. Using a structured review of research articles, industrial case studies, and technical reports from 2015 to 2025, the study evaluates key AI techniques including machine learning, computer vision, digital twins, and predictive modelling. Findings indicate that AI has improved ore recovery by up to 30% in smart sorting systems and reduced equipment downtime by as much as 50% through predictive maintenance. These results demonstrate AI’s ability to enhance both productivity and resource efficiency, though challenges related to data quality, system compatibility, and model interpretability persist. The review highlights the need for explainable AI, scalable digital twin architectures, and targeted workforce development to support wider adoption. Overall, the paper emphasizes the potential of AI to accelerate the transition toward intelligent, sustainable mining under the Mining 4.0 paradigm.

Digital Twins are emerging as a transformative technology within Cyber-Physical Systems (CPS), offering enhanced optimization, predictive maintenance, and real-time monitoring. However, their integration also introduces significant security challenges. These include vulnerabilities such as data breaches, unauthorized access, and cyber-attacks that disrupt real-time data flow between their physical and digital components. The involvement of IoT devices, sensors, and complex networked environments expands the attack surface, making Digital Twins susceptible to threats like Distributed Denial-of-Service (DDoS) attacks, malware infiltration, and insider sabotage. Effective risk management and assessment are crucial in identifying vulnerabilities, evaluating risks, and implementing mitigation strategies. Securing Digital Twins ensures data integrity, system reliability, and the continued functionality of the physical assets they represent. This paper aims to classify the various security threats associated with Digital Twins and propose structured risk management approaches to enhance their security within CPS. By addressing these challenges, organizations can ensure the dependability and trustworthiness of Digital Twin implementations across industries such as manufacturing, healthcare, smart cities, and IoT ecosystems.

Predictive maintenance (PdM), supported by artificial intelligence (AI) and digital twin methods, is gaining attention as a practical and cost-efficient way to manage power generation assets. In the renewable energy sector, where performance, stability, and cost control are central concerns, PdM enables operators to anticipate equipment faults, schedule interventions more effectively, and reduce unplanned downtime. This paper reviews how such approaches are being applied in four different national contexts: China, Germany, Norway, and the Netherlands, and considers their contribution to cleaner and more reliable energy systems. The discussion highlights several patterns that emerge across these countries. In China, the rapid expansion of wind and solar capacity has driven the use of PdM to improve fault detection and optimize turbine and panel performance. Germany demonstrates how PdM can be integrated into broader energy transition policies, using digital twins and AI to balance fluctuating renewable output with grid demands. Norway shows the value of predictive tools in extending the life and efficiency of hydropower equipment, while the Netherlands illustrates the benefits of PdM in offshore wind projects, where remote monitoring and early fault recognition are critical. Evidence from these cases points to three consistent outcomes: improved uptime of renewable assets, measurable reductions in maintenance costs, and smoother integration of intermittent power sources through more advanced grid management. Taken together, these findings suggest that PdM is not only a set of technical tools but also a strategic component in building sustainable, resilient, and economically viable energy systems. Its wider adoption may help accelerate the transition toward low-carbon power on a global scale.