Aims and Scope

Digital Twin Dynamics (DTD) is a peer-reviewed, interdisciplinary journal dedicated to advancing the science, engineering, and applications of digital twin technology across industries. Digital twins—virtual replicas of physical systems that enable real-time monitoring, simulation, and optimization—are transforming fields such as manufacturing, healthcare, smart cities, and aerospace. DTD provides a platform for cutting-edge research on the development, validation, and deployment of digital twins, emphasizing AI-driven modeling, real-time data integration, and cyber-physical system interoperability. The journal fosters innovation by bridging theoretical advancements with practical implementations, while addressing challenges in scalability, security, and standardization. We welcome contributions that explore the full lifecycle of digital twins, from design and simulation to predictive maintenance and autonomous decision-making. By promoting cross-sector collaboration, DTD aims to accelerate the adoption of digital twin technology in industry, academia, and government.

Topics of interest to the journal include, but are not limited to:

• Fundamentals & Modeling
• AI & Machine Learning for Digital Twins
• Industry-Specific Applications
• Real-Time Integration & IoT
• Security, Ethics & Governance
• Emerging Trends

ISSN: Applying

Frequency: Semi-yearly (June, December)