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Advancing Digital Engineering: Transforming Industries with Model-Based Systems Engineering and...
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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: 30 June 2026 | Viewed by 7675

Special Issue Editors

Dr. Sergio Luna

E-Mail Website
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
Prof. Dr. Ahsan R. Choudhuri

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
Dr. Aditya Akundi

E-Mail Website
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

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 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • digital engineering
  • enterprise transformation
  • model-based systems engineering
  • digital twin
  • AI-Driven systems engineering
  • digital thread

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Published Papers (5 papers)

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Research

45 pages, 6749 KB  
Article
An Ontology-Based Architecture for Interoperable Healthcare Systems-of-Systems: Structure, Interaction Patterns, and Covenant-Based Governance
by Mohamed Mogahed and Mo Mansouri
Systems 2026, 14(4), 376; https://doi.org/10.3390/systems14040376 - 31 Mar 2026
Viewed by 604
Abstract
Healthcare fragmentation—characterized by poor coordination among independently operating organizations—systematically degrades care quality while escalating costs. While healthcare delivery inherently operates as a System of Systems (SoS), existing approaches lack semantic rigor to bridge governance principles with implementable architectures, and digital engineering paradigms remain [...] Read more.
Healthcare fragmentation—characterized by poor coordination among independently operating organizations—systematically degrades care quality while escalating costs. While healthcare delivery inherently operates as a System of Systems (SoS), existing approaches lack semantic rigor to bridge governance principles with implementable architectures, and digital engineering paradigms remain disconnected from formal representations of regulatory constraints and organizational interdependencies. This paper presents a comprehensive Web Ontology Language (OWL 2 DL)-based ontology integrating structural, behavioral, and regulatory dimensions of healthcare SoS into a unified, computationally tractable framework. Developed following the Methontology engineering methodology and validated using the HermiT reasoner, the ontology formalizes constituent system categories through functional decomposition, establishes an interaction taxonomy distinguishing intra-category coordination from inter-category integration, and introduces the Covenant class as a novel governance mechanism. The covenant embeds legal frameworks (HIPAA, GDPR), interoperability protocols (FHIR, HL7), and technical standards (SNOMED, LOINC, ICD-11, ISO) as first-class ontological entities with explicit relationships to interaction properties. Governance enforcement is operationalized through a layered validation architecture comprising SWRL rules for deductive compliance checking, SHACL shapes for structural constraint validation, and OWL equivalentClass axioms for automated conflict detection. The ontology is further validated through four operational scenarios that demonstrate automated consent validation, standards compliance verification, protocol interoperability checking, and temporal compliance with conflict detection, alongside extended SPARQL queries that reveal constituent system landscapes, standards coverage, interaction networks, and topological properties through node degree calculation, hub identification, and network density analysis. The ontology enables pre-implementation governance assessments, evidence-based policy simulation, digital twin implementations with continuous compliance monitoring, and resilience planning through network analysis, transforming governance from reactive compliance checking to proactive coordination engineering. Full article
(This article belongs to the Special Issue Advancing Digital Engineering: Transforming Industries with Model-Based Systems Engineering and Digital Twin Technology)
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23 pages, 1483 KB  
Article
Digital Twin Integration for Enhancing Robotic Fastening Systems in Industrial Automation
by Eliasaf Levi, Sigal Kordova and Meir Tahan
Systems 2026, 14(4), 372; https://doi.org/10.3390/systems14040372 - 31 Mar 2026
Viewed by 474
Abstract
Digital twin (DT) technologies are increasingly applied in manufacturing to support monitoring, optimization, and predictive maintenance; however, most implementations remain operationally focused and disconnected from system-level decision-making and lifecycle engineering. This limitation is particularly critical in manufacturing environments that exhibit System-of-Systems (SoS) characteristics, [...] Read more.
Digital twin (DT) technologies are increasingly applied in manufacturing to support monitoring, optimization, and predictive maintenance; however, most implementations remain operationally focused and disconnected from system-level decision-making and lifecycle engineering. This limitation is particularly critical in manufacturing environments that exhibit System-of-Systems (SoS) characteristics, where performance emerges from the interactions among autonomous, interdependent subsystems. This study proposes an integrated systems engineering framework in which the digital twin functions as a system-level integrator rather than a standalone simulation tool. The framework embeds Quality Function Deployment (QFD), Analytic Hierarchy Process (AHP), Reliability and Safety analysis (RAMST), and Statistical Process Control (SPC) within a unified digital twin architecture, enabling explicit traceability from stakeholder requirements to design decisions, operational control, and lifecycle performance. The framework is demonstrated through a robotic fastening system operating under high variability, multi-vendor integration, and reliability constraints. A high-fidelity digital twin was developed in MATLAB Simscape and synchronized with operational data via virtual sensors and SPC-based monitoring. Results from a 35-month simulation study (n = 1050 operations) show a 30% reduction in system downtime and a 15% improvement in fastening quality (torque and angle compliance), supported by 95% confidence intervals, alongside enhanced fault detection and preventive maintenance capabilities. The findings demonstrate that integrating decision-making, monitoring, and learning within a single DT environment supports resilient, adaptive manufacturing systems aligned with Industry 4.0–5.0 objectives. Full article
(This article belongs to the Special Issue Advancing Digital Engineering: Transforming Industries with Model-Based Systems Engineering and Digital Twin Technology)
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65 pages, 3342 KB  
Article
Integrating ESG with Digital Twins and the Metaverse: A Data-Driven Framework for Smart Building Sustainability
by Nicola Magaletti, Chiara Tognon, Mauro Di Molfetta, Angelo Zerega, Valeria Notarnicola, Ettore Zini and Angelo Leogrande
Systems 2025, 13(12), 1083; https://doi.org/10.3390/systems13121083 - 1 Dec 2025
Cited by 2 | Viewed by 1241
Abstract
This article proposes a complex solution to improve sustainable intelligent building management based on the principles of Environmental, Social, and Governance (ESG) factors. The ESG KPI Framework–Metaverse-Enabled Operations incorporates the latest digital twin solutions, IoT sensor systems, and metaverse platforms to deliver real-time [...] Read more.
This article proposes a complex solution to improve sustainable intelligent building management based on the principles of Environmental, Social, and Governance (ESG) factors. The ESG KPI Framework–Metaverse-Enabled Operations incorporates the latest digital twin solutions, IoT sensor systems, and metaverse platforms to deliver real-time management and optimization of ESG factors. A hybrid solution strategy has been used in this framework, focusing on auto-acquisition of information and multiple validations at different levels through correlation analysis, Principal Component Analysis (PCA), Ordinary Least Squares (OLS) regression, and Machine Learning. The designed prototype links all the solutions together in a multi-level dashboard to represent key performance factors such as carbon footprint, energy consumption, renewable energy use, and occupant wellness. Experiments conducted validate the effectiveness of the proposed solution in improving prediction efficiency and user interaction experience during metaverse simulations. Full article
(This article belongs to the Special Issue Advancing Digital Engineering: Transforming Industries with Model-Based Systems Engineering and Digital Twin Technology)
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20 pages, 1623 KB  
Article
Industry-Driven Model-Based Systems Engineering (MBSE) Workforce Competencies—An AI-Based Competency Extraction Framework
by Aditya Akundi, Phani Ram Teja Ravipati, Sergio A. Luna Fong and Wilkistar Otieno
Systems 2025, 13(9), 781; https://doi.org/10.3390/systems13090781 - 5 Sep 2025
Viewed by 2490
Abstract
Model-based systems engineering (MBSE) is being rapidly adopted in U.S. industries across various sectors. While practitioners and academics recognize many benefits of adopting MBSE, industries also report challenges such as limited tool expertise and a shortage of skilled personnel. Highlighting the difficulties in [...] Read more.
Model-based systems engineering (MBSE) is being rapidly adopted in U.S. industries across various sectors. While practitioners and academics recognize many benefits of adopting MBSE, industries also report challenges such as limited tool expertise and a shortage of skilled personnel. Highlighting the difficulties in industry adoption of MBSE, prior research by the authors identified challenges such as tool limitations, knowledge gaps, cultural and political barriers, costs, and the level of customer understanding and acceptance of MBSE practices. Additionally, another study by the authors points out a gap between industry demands for MBSE skills in new hires and the current academic training programs. To further assess the MBSE industry’s workforce needs, this paper introduces a two-phase method for the Structured Extraction of MBSE competencies using large language models based on current workforce demands from LinkedIn job postings. Phase 1 involved extracting 1960 job descriptions from LinkedIn using the term “model-based systems engineer.” In phase 2, large language models (LLMs) employing deep transformer architectures were used to transform unstructured text into structured data. An AI agent was used as an autonomous software layer to manage every interaction between the raw dataset from Phase 1 and the LLM. Supported by the analyzed data, a competency framework is proposed that summarizes the tools, technical skills, and soft skills expected of a model-based systems engineer by the industry. The framework is designed to include core competencies shared across all MBSE roles, with specific competencies tailored for aerospace & defense, manufacturing and automotive, and software and IT sectors. Full article
(This article belongs to the Special Issue Advancing Digital Engineering: Transforming Industries with Model-Based Systems Engineering and Digital Twin Technology)
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13 pages, 2104 KB  
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 1747
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
(This article belongs to the Special Issue Advancing Digital Engineering: Transforming Industries with Model-Based Systems Engineering and Digital Twin Technology)
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