Submission Deadline: 30 September 2026 View: 63 Submit to Special Issue
Prof. Antonella Petrillo
Email: antonella.petrillo@uniparthenope.it
Affiliation: Department of Engineering, University of Naples "Parthenope", Napoli, 80143, Italy
Research Interests: AI, digitalization, sustainability, multi-criteria decision analysis
Prof. Fabio De Felice
Email: fabio.defelice@uniparthenope.it
Affiliation: Department of Engineering, University of Napoli "Parthenope", Naples, 80143, Italy
Research Interests: AI,smart manufacturing, innovation
The concept of Digital Twins (DTs) has emerged as a transformative paradigm in engineering and applied sciences, enabling the development of high-fidelity virtual representations of physical systems that evolve dynamically through continuous data integration. By tightly coupling physics-based computational models, data-driven methods, and real-time information, Digital Twins provide a powerful foundation for predictive analysis, optimization, and decision making throughout the lifecycle of engineering systems.
Recent advances in computational mechanics, multiphysics modeling, high-performance computing, and artificial intelligence have significantly expanded the scope and impact of Digital Twins and Virtual Engineering Systems. In particular, the growing emphasis on sustainability and resilience in engineering has positioned Digital Twins as key enablers for reducing environmental impact, improving resource efficiency, extending system lifetime, and supporting informed decision-making aligned with sustainability goals.
At the same time, the rapid evolution of Large Language Models (LLMs) and generative AI introduces new opportunities for Digital Twin development and deployment. LLMs can support intelligent model orchestration, automated knowledge extraction from engineering data and documentation, enhanced human–machine interaction, and the integration of heterogeneous simulation, monitoring, and control workflows. When combined with physics-informed and data-driven models, LLMs can facilitate explainable, scalable, and adaptive Digital Twin ecosystems.
Despite these advances, critical challenges remain in ensuring robust, reliable, and interpretable decision making, particularly in the presence of uncertainty, incomplete data, and computational constraints. Addressing these challenges requires innovative methodologies in computational modeling, uncertainty quantification, optimization, and decision science.
This Special Issue aims to provide a comprehensive forum for original contributions on Digital Twins and Virtual Engineering Systems as enablers of intelligent, sustainable, and data-informed decision making, with an emphasis on advanced computational methods and real-world engineering applications.
Topics of Interest (include but are not limited to)
• Digital Twin frameworks for sustainable engineering systems
• Virtual engineering systems and cyber–physical systems
• Physics-based, data-driven, and hybrid modeling for Digital Twins
• Multiphysics and multiscale computational models
• AI- and machine-learning-enhanced Digital Twins
• Integration of Large Language Models (LLMs) in Digital Twin workflows
• Integration of Large Language Models (LLMs) in decision-making workflows
• LLM-assisted model management, simulation orchestration, and knowledge representation
• Human–Digital Twin interaction and explainable AI for engineering systems
• Model updating, calibration, and sensor data assimilation
• Uncertainty quantification, reliability, and trustworthiness in Digital Twins
• Reduced-order and surrogate modeling for real-time and energy-efficient computation
• High-performance and green computing strategies for Digital Twins
• Digital Twins for lifecycle assessment, energy efficiency, and carbon footprint reduction
• Predictive maintenance, resilience, and circular engineering systems
• Applications in structural mechanics, fluid dynamics, materials, energy, manufacturing, and smart infrastructure
• Sustainable and resilience-oriented decision making
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