Towards Virtualized Intelligence: Digital Twins Powered by AI and Sensors
A special issue of Technologies (ISSN 2227-7080).
Deadline for manuscript submissions: 30 April 2026 | Viewed by 115
Special Issue Editors
Interests: web/pervasive computing; human activity recognition; sensor-based preventive healthcare application; database
Interests: machine learning; artificial intelligence; sensors; healthcare; decision support
Interests: human activity recognition; indoor localization; data augmentation; machine learning; data science; applied artificial intelligence
Special Issue Information
Dear Colleagues,
Digital twins have emerged as a transformative paradigm for representing, monitoring, and optimizing physical systems through their virtual counterparts. Unlike static models, digital twins are dynamic, continuously updated with real-world data streams from heterogeneous sensors, enabling a high-fidelity reflection of the physical environment. This integration allows real-time insights into system states, early detection of anomalies, and exploration of "what-if" scenarios in a safe digital space. The fusion of digital twins with artificial intelligence (AI) further amplifies their potential by providing the ability to learn from complex multimodal data, uncover hidden patterns, and support predictive and prescriptive decision-making. AI-driven digital twins can adapt over time, simulate future trajectories, and optimize operations in ways that exceed traditional model-based approaches.
Sensors play a central role in this ecosystem by serving as the primary interface between the physical and digital domains. Advances in sensor technology, including miniaturization, multimodal integration, and ubiquitous deployment, are rapidly expanding the applicability of digital twins in fields such as robotics, healthcare, and industrial automation. When combined with AI, sensor-enriched digital twins enable unprecedented opportunities for personalized medicine, predictive maintenance, human–robot interaction, and resilient infrastructure.
This Special Issue aims to gather contributions that advance the state of the art in AI-enabled digital twins with sensor integration. We invite original research, surveys, and case studies that explore methodological innovations, computational frameworks, and domain-specific applications. By bridging AI, sensing, and digital twinning, this Special Issue seeks to provide a forum for interdisciplinary dialogue and to chart a path towards intelligent, adaptive, and sustainable systems of the future.
Dr. Sozo Inoue
Dr. Björn Friedrich
Guest Editors
Dr. Christina Alvarez Garcia
Guest Editor Assistant
Manuscript Submission Information
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Keywords
- artificial intelligence
- machine learning
- digital twins
- intelligent sensing
- data-driven modelling
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