Advancing Digital Engineering: Transforming Industries with Model-Based Systems Engineering and Digital Twin Technology
A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Artificial Intelligence and Digital Systems Engineering".
Deadline for manuscript submissions: 31 October 2025 | Viewed by 145
Special Issue Editors
Interests: model-based systems engineering; enterprise transformation; digital engineering
Special Issues, Collections and Topics in MDPI journals
Interests: aerospace and defense systems
Interests: model-based systems engineering; cyber-physical systems; digital quality control; engineering education
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Industries across aerospace and defense, manufacturing, automotive, energy, telecommunications, and other domains, are undergoing rapid transformation due to evolving technological landscapes, increasing system complexity, and shifting paradigms in systems engineering and lifecycle management. Modern systems must support greater functionality, exponential growth in interfaces, and real-time, data-driven decision-making, all while ensuring scalability, reliability, and interoperability across multiple domains.
As system complexity increases, existing engineering methodologies, tools, and processes struggle to keep up with demands for automation, adaptation, and digital system thread integration. Addressing these challenges requires a fundamental transformation in how systems are conceptualized, designed, developed, tested, and sustained. The convergence of Digital Engineering, Model-Based Systems Engineering (MBSE), and Digital Twin Technology presents a powerful framework to enhance system modeling, simulation fidelity, predictive analytics, and lifecycle adaptability.
This Special Issue invites contributions that push the boundaries of Digital Engineering, MBSE, and Digital Twin Environments by presenting novel theories, methodologies, architectures, and real-world applications. We invite original research articles, case studies, comparative analyses, and critical reviews that advance the next generation of intelligent, interconnected, and adaptive complex systems.
This Special Issue is particularly interested in articles in the following areas:
- MBSE-Driven Digital Twin Architectures – Scalable, modular, and interoperable frameworks for complex systems.
- Digital Thread & Interoperability Standards – Integration of MBSE, digital twins, IoT, and AI-driven analytics.
- AI-Augmented MBSE & Digital Twins – Machine learning, knowledge graphs, and data-driven intelligence for digital engineering.
- Verification, Validation, and Trust – Ensuring model fidelity, cybersecurity, and uncertainty quantification in digital twin environments.
- Lifecycle-Centric Digital Twin Ecosystems – System integration, decision support, and human-in-the-loop collaboration.
- Cross-Domain Engineering Applications – Implementing MBSE and digital twins in aerospace, automotive, healthcare, and industrial systems.
- Enterprise Digital Transformation & Adoption – Strategies for workforce adaptation, ROI assessment, and standardization in digital engineering.
Dr. Sergio Luna
Prof. Dr. Ahsan R. Choudhuri
Dr. Aditya Akundi
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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Keywords
- digital engineering
- enterprise transformation
- model-based systems engineering
- digital twin
- AI-Driven systems engineering
- digital thread
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