About The Special Session

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The advent of digital twins has revolutionized the simulation and optimization of real-world scenarios. Digital twins are comparable virtual replicas of real-world systems, assets, or processes that allow for real-time optimization, simulation, and monitoring. Through a complete or semi-complete digital replication of a physical object, they provide performance analysis, problem prediction, and scenario testing without affecting the real system. When combined with deep learning, these virtual replicas gain the ability to learn from extensive data, adapt to changing conditions, and predict future states with exceptional precision. This integration enables digital twins to not only reflect their physical counterparts but also anticipate issues, enhance performance, and autonomously support decision-making processes. The applications are extensive: in manufacturing, it leads to smart factories where production lines optimize themselves for efficiency; in healthcare, patient-specific digital twins can forecast health trajectories and tailor treatments; in urban planning, city-wide digital twins can model traffic and energy use to improve sustainability. Deep learning allows digital twins to become dynamic entities that change in tandem with their physical counterparts, creating interesting prospects for innovation in a variety of research and application areas.

Topics

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The topics of interest are inspired by the themes above and include, but are not limited to:

• Healthcare and Personalized Medicine
• Predictive Maintenance and Anomaly Detection
• Smart Agriculture, Autonomous Systems and Robotics, Smart Cities and Infrastructure Management, Simulation and Virtual Environments, as well as Supply Chain Optimization
• Scalability and Federated Learning for Distributed Digital Twins
• Natural Language Processing for Human-Digital Twin Interaction
• Deep Learning in Digital Twin Cybersecurity and Privacy-Preserving Deep Learning
• Data Fusion and Multi-modal Learning into Digital Twin systems
• Generative Models for Synthetic Data Generation in Digital Twins
• Continuous Learning and Adaptation in Digital Twins
• Explainable AI (XAI) for Digital Twins
• Transfer Learning for Digital Twin Customization