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 566

Special Issue Editors


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Guest Editor
Industrial, Manufacturing and Systems Engineering Department, The University of Texas at El Paso, El Paso, TX , USA
Interests: model-based systems engineering; enterprise transformation; digital engineering
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Aerospace and Mechanical Engineering Department, The University of Texas at El Paso, El Paso, TX, USA
Interests: aerospace and defense systems

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Guest Editor
Industrial and Manufacturing Engineering, University of Wisconsin—Milwaukee, Milwaukee, WI, USA
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

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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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Published Papers (1 paper)

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Research

13 pages, 2104 KiB  
Article
Test and Evaluation of AI/ML Enhanced Digital Twin
by Mario Reyes Garcia, Jesus Castillo and Afroza Shirin
Systems 2025, 13(8), 656; https://doi.org/10.3390/systems13080656 - 4 Aug 2025
Viewed by 226
Abstract
A Digital Twin (DT) is not just a collection of static digital models at the component level of a physical system, but a dynamic entity that evolves in parallel with the physical system it mirrors. This evolution starts with physics-based or data-driven physics [...] Read more.
A Digital Twin (DT) is not just a collection of static digital models at the component level of a physical system, but a dynamic entity that evolves in parallel with the physical system it mirrors. This evolution starts with physics-based or data-driven physics models representing the physical system and advances to Authoritative Virtualization or DT through continuous data assimilation, and ongoing Digital Engineering (DE) Test and Evaluation (T&E) processes. This paper presents a generalizable mathematical framework for the DE Test and Evaluation Process that incorporates data assimilation, uncertainty quantification, propagation, and DT calibration, applicable to diverse physical–digital systems. This framework will enable the DT to perform operations, control, decision-making, and predictions at scale. The framework will be implemented for two cases: (i) the DT of the CubeSat to analyze the CubeSat’s structural deformation during its deployment in space and (ii) the DT of the CROME engine. The DT of the CubeSat will be capable of predicting and monitoring structural health during its space operations. The DT of the CROME engine will be able to predict the thrust at various conditions. Full article
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