Computational Methods for Advanced Digital Twins in Biological and Engineered Systems

Dear Colleagues,

Digital Twin (DT) models have emerged as a foundational paradigm for capturing, simulating, and interpreting the behavior of both biological and engineered systems.

In the context of Human Digital Twins (HDTs), current research advances are increasingly directed toward the representation of anatomical, biomechanical, and physiological processes for applications in personalized medicine, ergonomics, rehabilitation, and human–machine interaction. These models require multiscale formulations, individualized parameterization, and the integration of heterogeneous biological and biomechanical data sources.

Concurrently, Industrial Digital Twins (IDTs) have achieved widespread adoption across manufacturing, aerospace, and operational settings to support predictive maintenance, process optimization, real-time monitoring, and model-based decision making. Their construction relies on the integration of sensor networks, IoT infrastructures, physics-based simulation frameworks, and machine-learning-based inference mechanisms, which collectively furnish the information required for the continuous updating of the virtual counterpart.

A methodological challenge of central relevance to both HDTs and IDTs concerns the development of parameterized, dynamically updatable, computationally scalable, and inherently human-aware Digital Twins that can reproduce complex system dynamics while supporting trustworthy decision processes. Addressing this challenge requires advances in parameter identification, data assimilation, uncertainty quantification, model reduction, multimodal integration, and real-time computational strategies. Extended Reality (XR) technologies provide complementary avenues for the operational exploitation of DTs, offering advanced interaction, visualization, and operator-in-the-loop modalities.

This Special Issue seeks contributions that advance the methodological and algorithmic foundations underlying the generation of parameterized, scalable, and continuously updatable Digital Twins for human and engineered systems. We invite submissions on topics including, but not limited to, the following:

• Computational modeling and simulation frameworks
• Human Digital Twins
• Parameter identification and model updating
• Multiscale and multimodal data integration
• Machine learning-assisted DT construction
• Real-time and near-real-time computational strategies
• XR interfaces for DT interaction and visualization
• Predictive maintenance and anomaly detection
• Operational assessment and decision support
• Simulation-based training environments
• Structural health monitoring
• Multiphysics modeling for product and process design
• Human-in-the-loop XR-enabled monitoring and control

Dr. Emanuele Guardiani
Dr. Fontana Carlotta
Dr. Michele Calì
Guest Editors

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