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Johan Stahre, Chalmers University of Technology, Sweden, Sweden

Generative Digital Twins: Principles, Architecture, Methodology, and Applications
Giancarlo Fortino, University Calabria, Italy, Italy

 

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Johan Stahre
Chalmers University of Technology, Sweden
 

Brief Bio
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Abstract
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Generative Digital Twins: Principles, Architecture, Methodology, and Applications

Giancarlo Fortino
University Calabria, Italy
 

Brief Bio
Giancarlo Fortino (IEEE Fellow 2022) is Full Professor of Computer Engineering at the Dept of Informatics, Modeling, Electronics, and Systems of the University of Calabria (Unical), Italy. He received a PhD in Computer Engineering from Unical in 2000. He is also a distinguished professor at Wuhan University of Technology (China), a high-end expert at Huazhong University of Science and Technology (China), a senior research fellow at the Italian ICAR-CNR Institute, CAS PIFI Group international fellow at SIAT (Shenzhen), and Distinguished Lecturer for IEEE Sensors Council, SMC society, and IoT TC. He was also a visiting researcher at ICSI, Berkeley (USA), in 1997 and 1999, and a visiting professor at Queensland University of Technology in 2009. At Unical, he is the chair of the PhD School in ICT, the director of the SPEME lab and of the Radiomics lab, and the director of the Postgraduate Master course in AI-driven Radiomics, as well as co-chair of Joint labs on IoT technologies established between Unical and the WUT, SMU, and HZAU Chinese universities, and the AI-driven Robotics Lab funded with the J.C. Bose University of Science and Technology, YMCA. Fortino is also the scientific responsible of the Digital Health group of the Italian CINI National Laboratory at Unical. He is a Highly Cited Researcher 2020-2025 in Computer Science by Clarivate (the only Italian professor ranked). He had 25+ highly cited papers in WoS, and an h-index of 88 with 33000+ citations in Google Scholar. His research interests include wearable computing systems, e-Health, Internet of Things, and agent-based computing. He is the author of 750+ papers in international journals, conferences, and books. He is (founding) series editor of IEEE Press Book Series on Human-Machine Systems and EiC of Springer Internet of Things series and AE of premier int'l journals such as IEEE TASE (senior editor), IEEE TAFFC-CS, IEEE THMS, IEEE T-AI, IEEE SJ, IEEE JBHI, IEEE OJEMB, IEEE OJCS, Information Fusion, EAAI, etc. He chaired many international workshops and conferences (130+), was involved in a huge number of international conferences/workshops (800+) as an IPC member, is/was a guest editor of many special issues (80+). He is cofounder and CEO of SenSysCal S.r.l., a Unical spinoff focused on innovative IoT systems, and, recently, cofounder and vice-CEO of the spin-off Bigtech S.r.l., focused on big data, AI, and IoT technologies. Fortino is the VP of Cybernetics (term 2026-2027) of the IEEE SMCS, member of the IEEE SMCS ExCom, and former chair of the IEEE SMCS Italian Chapter.


Abstract
Digital Twins (DTs) are software replicas that not only mirrors physical entities but can also proactively predict, control, optimize and simulate their behavior. Born in the manufacturing sector, this concept, after an initial hype, stayed untouched for decades. The rise of the Internet of Things (IoT) and Artificial Intelligence (AI) enabled DT, respectively, to exchange real-world data and to fully exploit it for fulfilling its own goals. Very recently, Generative AI (Gen-AI) methods started being sporadically applied to DT in different contexts and with different targets. In this talk, starting from our experiences on design, implementation, and evaluation of DTs and, more recently, of Opportunistic DTs, we first provide a definition for the Generative DT (GDT) which embraces the main distinctive aspects and potential of current and future Gen-Al-aided DTs. In particular, we disclose the role of Gen-AI in conciliating the model- and the data-driven approach for the development of DTs. Then, we analyze the added value of main Gen-AI architectures and development methodologies for maximizing the effectiveness and the performance of DTs operating in the IoT domain and deployed in the device-edge-cloud continuum. Finally, we illustrate the potential of GDT in emblematic use cases in the Smart City, Smart Manufacturing, Smart Water Systems, Smart Robotics, Smart Education, and, more in general, in Smart IoT-driven domains



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