The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Leveraging Digital Twin Technology in Model-Based Systems Engineering
Open accessAbstract
Digital twin, a concept introduced in 2002, is becoming increasingly relevant to systems engineering and, more specifically, to model-based system engineering (MBSE). A digital twin, like a virtual prototype, is a dynamic digital representation of a physical system. However, unlike a virtual prototype, a digital twin is a virtual instance of a physical system (twin) that is continually updated with the latter’s performance, maintenance, and health status data throughout the physical system’s life cycle. This paper presents an overall vision and rationale for incorporating digital twin technology into MBSE. The paper discusses the benefits of integrating digital twins with system simulation and Internet of Things (IoT) in support of MBSE and provides specific examples of the use and benefits of digital twin technology in different industries. It concludes with a recommendation to make digital twin technology an integral part of MBSE methodology and experimentation testbeds.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“These definitions are most inclusive and appropriate for the construction sector because they address the purpose of DT in this industryreal-time monitoring of construction projects and predictive maintenance.”
From A review of digital twin applications in construction (2022) · cites this paper as Madni et al. (2019)
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Data security and ownership Cyber threats such as access to confidential data [41].”
From Analysis of Digital Twin Applications in Energy Efficiency: A Systematic Review (2025) · cites this paper as [41]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Although a DT can require a great upfront investment, it can provide significant investment return in the long-run operation of a system (Madni et al, 2019).”
From A review of digital twin applications in construction (2022) · cites this paper as Madni et al, 2019
The cited source says what the claim attributes to it.
In the citing paper
“DT can predict a system's response to safety in cases of critical events and can reveal imminent issues before they become critical by comparing actual responses to predicted ones (Madni et al, 2019).”
From A review of digital twin applications in construction (2022) · cites this paper as Madni et al, 2019
The cited source says what the claim attributes to it.
In the citing paper
“While they enhance real-time remote monitoring and predictive maintenance [39], they also pose risks related to data security and interoperability when integrated with legacy systems [41,42].”
From Analysis of Digital Twin Applications in Energy Efficiency: A Systematic Review (2025) · cites this paper as [41]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Management techniques for large virtualized infrastructure [163] could be usefully adopted in these cases.”
From Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models (2020) · cites this paper as [163]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Digital Twin in manufacturing: A categorical literature review and classification
Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.08.474 · Cited by 3,612
The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles
Glaessgen, E., & Stargel, D. (2012) · 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA · doi:10.2514/6.2012-1818 · Cited by 2,404 · cited 3×
“Finally, the systems on board the digital twin are capable of mitigating damage or degradation by activating self-healing mechanisms, or by recommending changes in mission profile to decrease loads, thereby increasing both the life span and the probability of mission success [9].”
BackgroundInfluentialA Review of the Roles of Digital Twin in CPS-based Production Systems
Negri, E., Fumagalli, L., & Macchi, M. (2017) · Procedia Manufacturing · doi:10.1016/j.promfg.2017.07.198 · Cited by 1,669
The Digital Twin: Realizing the Cyber-Physical Production System for Industry 4.0
Uhlemann, T. H.-J., Lehmann, C., & Steinhilper, R. (2017) · Procedia CIRP · doi:10.1016/j.procir.2016.11.152 · Cited by 994
Haag, S., & Anderl, R. (2018) · Manufacturing Letters · doi:10.1016/j.mfglet.2018.02.006 · Cited by 559
Toward a Digital Twin for real-time geometry assurance in individualized production
Söderberg, R., Wärmefjord, K., Carlson, J. S., & Lindkvist, L. (2017) · CIRP Annals · doi:10.1016/j.cirp.2017.04.038 · Cited by 543
Experimentable Digital Twins—Streamlining Simulation-Based Systems Engineering for Industry 4.0
Schluse, M., Priggemeyer, M., Atorf, L., & Roßmann, J. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2804917 · Cited by 524
The Digital Twin: Demonstrating the Potential of Real Time Data Acquisition in Production Systems ☆
Uhlemann, T., Schock, C., Lehmann, C., Freiberger, S., & Steinhilper, R. (2017) · Procedia Manufacturing · doi:10.1016/j.promfg.2017.04.043 · Cited by 426
Model‐based systems engineering: Motivation, current status, and research opportunities
Madni, A. M., & Sievers, M. (2018) · Systems Engineering · doi:10.1002/sys.21438 · Cited by 424
Self-citationSchluse, M., & Rossmann, J. (2016) · 2016 IEEE International Symposium on Systems Engineering (ISSE) · doi:10.1109/syseng.2016.7753162 · Cited by 292
Flagged first, then by citations
Research works printed but not linked: 6
- Madni, A.M. (2017). Transdisciplinary Systems Engineering: Exploiting Convergence in a Hyper-Connected World, Springer. Foreword by Norm Augustine.
- Zeigler, B., Muzy, A., and Kofman, E. (2018). Theory of Modeling and Simulation: Discrete Event and Iterative System Computational Foundations, Academic Press. [3rd ed.].
- Madni, A.M., Sievers, M., Erwin, D., Madni, A., Ordoukhanian, E., and Pouya, P. (2019, January 7–11). Formal Modeling of Complex Resilient Networked Systems. Proceedings of the AIAA Science and Technology Forum, San Diego, CA, USA.
- Madni, A.M. (2018, January 22–24). Next Generation Adaptive Cyber-Physical Systems. Proceedings of the 21st Annual Systems Engineering Conference, Tampa, FL, USA.
- Madni, A.M., Madni, C.C., and Sievers, M. (2018, January 7–12). Adaptive Cyber-Physical-Human Systems. Proceedings of the 2018 INCOSE International Symposium, Washington, DC, USA.
- Kraft, E. (2017, January 25). Challenges and Innovations in Digital Systems Engineering. Proceedings of the NDIA 20th Annual Systems Engineering Conference, Springfield, VA, USA.
Cited by
Sorted by citations
Items 901–1,000 of 1,000
Towards a Digital Twin System for Human Crowd Motion Prediction
Alba, I., Troya, J., & Canal, C. (2025) · Lecture notes in business information processing · doi:10.1007/978-3-031-79059-1_22
Digital Twin-Driven Modeling and Application of High-Temperature Biaxial Materials Testing Apparatus
Gao, X., Liu, P., Zhao, A., Huang, G., Zhang, J., & Zhou, L. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.060194
Improving Assembly Lines with Digital Twins: A Synthetic Case Study
Juarez, M. G. J., Giret, A., & Botti, V. J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-80366-6_3
Uncovering key success factors for enhanced office-site communication through digital twins
Chalipour, A., Noorzai, E., Golabchi, M., & Nourzad, S. H. H. (2025) · The TQM Journal · doi:10.1108/tqm-04-2024-0146 · Cited by 1
Study on using digital twin technology for monitoring patients remotely
Feng, Y., Zhang, N., Wang, A., & Zhai, Y. (2025) · International Conference on Physics, Photonics, and Optical Engineering (ICPPOE) · doi:10.1117/12.3060454
Multimodal health data fusion and analysis based on digital twins
Guo, F., Dong, Q., Guo, T., & Zhang, X. (2025) · International Conference on Physics, Photonics, and Optical Engineering (ICPPOE) · doi:10.1117/12.3060429
Enhancing Mass Customization Using a Digital Twin: Review and Model Development
Bezuneh, M. K., Tsegaw, A. A., Woldegiorgis, B. H., Bogale, T. M., Brossog, M., & Franke, J. E. (2025) · Green energy and technology · doi:10.1007/978-3-031-77339-6_8
Mechanistische mathematische Modelle als Grundlage für digitale Zwillinge
Moser, A., Appl, C., Brüning, S., & Hass, V. C. (2025) · Springer eBooks · doi:10.1007/978-3-031-75698-6_6
Trends der Entwicklung von digitalen Zwillingen für die Bioproduktionsindustrie
Gargalo, C. L., Heras, S. C. D. L., Jones, M., Udugama, I. A., Mansouri, S. S., Krühne, U., & Gernaey, K. V. (2025) · Springer eBooks · doi:10.1007/978-3-031-75698-6_1
Design and Development of Digital Twin of Single Cell Manufacturing Unit using Delmia
Arvind, A., Kannan, M. M. K., Saravanan, D. S., Avinash, S. M., Ajay Srikanth, G., & Gurunathraj, E. (2025) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-25601
Digital Twins in the Metaverse
Malviya, R., & Rajput, S. (2025) · Wiley eBooks · doi:10.1002/9781394315680.ch1
Mission-Critical Facilities: Engineering Approaches for High Availability and Disaster Resilience
Patel, R. (2025) · Asian Journal of Computer Science Engineering · doi:10.22377/ajcse.v10i2.212
Tarihi Yapıların Sürdürülebilirliğinde Dijital İkiz Kullanımı: Bir Çoklu Durum Çalışması
Akgöz, G., & Ergin, G. (2025) · Dokuz Eylül Üniversitesi Güzel Sanatlar Fakültesi Dergisi · doi:10.17484/yedi.1648438
Puppala, A. (2025) · European Journal of Computer Science and Information Technology · doi:10.37745/ejcsit.2013/vol13n4496103 · Cited by 1
Production Process and Digital Twin
Червяков, Л., Oleynik, А. V., Zhdanov, E. R., & Ragutkin, A. V. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-95649-2_39
A Value Driven Digital Twin for Academic Purposes
Asranov, M., Serio, F., Kayumov, M., & Chiabert, P. (2025) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-93319-6_18
Model-Based Development and Integration of a Digital Twin for a Cobotic Spinal Surgery System
Chaieb, A., Mhenni, F., Mlika, A., & Choley, J. (2025) · Mechanisms and machine science · doi:10.1007/978-3-031-96081-9_32
Haryanto, H. (2025) · sudo Jurnal Teknik Informatika · doi:10.56211/sudo.v4i2.672
Romsaiyud, W., Konglerd, P., & Takaew, S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0746-6.ch010
أمكانية تطبيق التوأمة الرقمية لتحسين الأداء التنظيمي
القصير, ن. أ., & أرحيم, ع. ع. (2025) · Journal of Kufa Studies Center · doi:10.36322/jksc.177(b).20395
Barker, G., & Abdoli, S. (2025) · Procedia CIRP · doi:10.1016/j.procir.2025.08.097
Koudri, A. (2025) · doi:10.1002/9781394345106.ch02
Koudri, A. (2025) · doi:10.1002/9781394345106.ch04
Jiang, B., Guo, Z., Chang, C., Zhang, J., & Li, L. (2025) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2025.3601514
Rakotonirina, H.-Y., Charki, A., Espès, D., Costanzo, A., & Chokr, B. (2026) · Array · doi:10.1016/j.array.2026.101175
A Review of Digital Twins for Electric Vehicles
Zhou, Y., & Huang, C. (2025) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-96146-5_28
BRIDGING REALITIES: BRINGING MBSE MODELS TO LIFE WITH DIGITAL TWINS
Bass, H., Cantor, C., Flint, J., Marx, W. J., Rogers, J., & Cooper, C. (2025) · INCOSE International Symposium · doi:10.1002/iis2.70016
Shiling, Z., Xiao, Q., Jungang, W., & Yang, F. (2025) · International Conference on Smart Grid and Sustainable Energy (SGSE) · doi:10.1109/sgse66281.2025.11200445
Hu, Y., Zhang, Z., Yang, X., Li, X., & Wang, Z. (2025) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-025-16816-w
Pereira, M., Leão, C. P., & da Costa, S. P. (2025) · EAI Endorsed Transactions on Digital Transformation of Industrial Processes · doi:10.4108/dtip.9821
Wang, H., & Tsai, Y. J. (2025) · doi:10.1145/3764924.3770893
Shridhar, S., Garg, S., Rajesh, S. K., & Bhat, A. (2025) · AIP conference proceedings · doi:10.1063/5.0294273
Benchmarking Digital Twins for Tower Cranes: Isaac Sim vs. Gazebo
dos Santos, J., Bicho, M. C., Froes, T., Dorneles, G. A., Botelho, S. S. D. C., Gonçalves, E. M. N., & Pias, M. R. (2025) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon58223.2025.11221823
From Physics-of-Failure to Physics-of-Degradation for Microelectronic Devices
van Driel, W. D., & Fan, J. (2025) · IEEE reliability magazine · doi:10.1109/mrl.2025.3628596
Závadský, J., Závadská, Z., Osvaldová, Z., & Hiadlovský, V. (2025) · Processes · doi:10.3390/pr13123799
Balomenos, A. D., Stefanou, V., & Manolakos, E. S. (2021) · Figshare · doi:10.6084/m9.figshare.16907599
A Holistic Approach to Managing the Iron Gate Hydropower and Navigation System
Institut za vodoprivredu Jaroslav Černi: Beograd, B., Milivojevic, N., Divac, D., Institut za vodoprivredu Jaroslav Černi, B., Stojanović, B., Marjanovic, P., Institut za vodoprivredu Jaroslav Černi, B., Majkić-Dursun, B., Institut za vodoprivredu Jaroslav Černi, B., Stankovic, D., et al. (2025) · Energija Ekonomija Ekologija · doi:10.46793/eee25-3.07m
Velasquez, J. M. R., & Iordanova, I. (2025) · Lecture notes in civil engineering · doi:10.1007/978-3-031-97697-1_31 · Cited by 1
Innovation in Communication and Networking Field: Luminous Future as Metaverse
Dubey, S., Sengar, V., Singh, S., Dubey, M., & Saxena, S. P. (2025) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83662-666-420261003
Zhang, H., Wang, W., & Wen, Y. (2025) · Systems Engineering · doi:10.1002/sys.70033
Wang, C., Sai, A. M. V. V., Wang, K., & Li, Y. (2025) · Intelligent and Converged Networks · doi:10.23919/icn.2025.0021
47Chapter 3 Integrating AI and digital twin technology in cancer therapy
Malviya, R., Rajput, S., Roy, M., Sridhar, S. B., & Thomas, S. (2025) · De Gruyter eBooks · doi:10.1515/9783111670744-003
Digital Twin Integration in Automotive Model Based System Engineering
Agarwal, U., & Sabharwal, S. (2026) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2026-26-0379
Chan, A. T. K., Wang, M., Brilakis, I., & Ip, N. M. Y. (2026) · ASCE International Conference on Computing in Civil Engineering (i3CE) · doi:10.1061/9780784486436.083
Decision Support Systems for Smart Manufacturing Using Digital Twins
Isiaku, L., Karagözlü, D., & Hamada, A. (2026) · Signals and communication technology · doi:10.1007/978-3-031-89700-9_3
Spadoni, F., Frabetti, P., Bici, M., & Campana, F. (2026) · Lecture notes in mechanical engineering · doi:10.1007/978-3-032-14950-3_34
Hyperdimensional computing for sustainable manufacturing: an initial assessment
Hoang, D., Patel, A., Chen, R. S., Malhotra, R., & Imani, F. (2026) · Manufacturing Letters · doi:10.1016/j.mfglet.2026.01.004
Nishad, S. K., Singh, A. K., & Sahu, A. K. (2026) · Global Business Review · doi:10.1177/09721509261420072 · Cited by 1
Digital Twins and Predictive Analytics for Next-Generation Power Transmission Systems
Sayed, K., Elsayed, M. A. E.-H., Kirakosian, D., & Abokhalil, A. G. (2025) · doi:10.1109/njfet67489.2025.11380514
Digital Twin Framework for Air Conditioning
Almulla, A., Alkhateeb, H. K., Alghamdi, R., Ansari, S., Almulhim, K., Aldbass, S., & Almutairi, R. (2025) · Middle East Corrosion Conference (MECC) · doi:10.5006/m2025_00678
Komoto, H., & Stark, R. (2026) · CIRP journal of manufacturing science and technology · doi:10.1016/j.cirpj.2026.02.004
Díaz, D., Boj, C., & Pérez-Liébana, D. (2026) · AI & Society · doi:10.1007/s00146-026-02911-3
Design Method of Complex System Human-Machine Integration Architecture for MBSE
熊成云, Hu, C., Li, M., & Jiang, J. (2025) · IEEE International Conference on Artificial Intelligence, Computer Science and Information Processing (AICSIP) · doi:10.1109/aicsip65423.2025.11427226
Digital twins in Chinese startups: Enhancing precision and efficiency in the entrepreneurial process
Kouam, A. W. F. (2026) · Technology in Society · doi:10.1016/j.techsoc.2026.103307
Cognitive Digital Twins for Predictive AI-Driven Operations
Hadi, A., Pala, P., Govindarajan, S., Janapareddy, N. V. P., & Mishra, A. (2025) · Asian Conference on Intelligent Technologies (ACOIT) · doi:10.1109/acoit66109.2025.11436794
AI and Digital Twin-Driven Dynamic Optimization for Electromechanical Systems
Zhang, F. (2025) · IEEE International Conference on Electrical, Automation and Computer Engineering (ICEACE) · doi:10.1109/iceace67491.2025.11439327
Digital Twin and IoT Integration for Predictive Maintenance in Civil and Structural Engineering
Leong, W. Y. (2026) · IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE) · doi:10.3390/engproc2026134019 · Cited by 3
Digital twins in traumatology and orthopedics: review of joint imaging and cost-effectiveness
Вячеславович, П. А., Призов, А. П., Petraikin, A. V., Muraev, A. A., Zagorodniy, N. V., Almazov, A., Kadyrov, F. N., & Дмитриевич, Г. В. (2026) · Journal of clinical practice · doi:10.17816/clinpract688314
Semeraro, C. (2020) · theses.fr (ABES) · doi:10.70675/444f6160z43f3z4541zbc9fz2686f477f659
Reality Anchor Methodology : Designing a digital twin supporting situation awareness
Pinto, S. C. D. (2022) · theses.fr (ABES) · doi:10.70675/53247eadz13fez4acaz93ebz4668ddcb31c3 · Cited by 2
A vision-based mixed-reality framework for testing autonomous driving systems
Argui, I. (2024) · theses.fr (ABES) · doi:10.70675/7107c9e5z023dz40e9zb7edz499c7677d063
Digital Twin-Driven Innovation in the Teaching Model of Intelligent Manufacturing Practical Training
Liao, G., Chen, Z., Shen, Y., Zhang, Y., & Wang, Z. (2026) · International Conference on Informatics Education and Computer Technology Applications (IECA) · doi:10.1145/3802133.3802242
Mohammed, R. H., Al-Ghazali, B. M., Alaghbari, M. A., Alawag, A. M., Al-Azazi, S. A., & Althawadi, M. (2025) · International Conference on Decision Aid Sciences and Applications (DASA) · doi:10.1109/dasa68193.2025.11498752
Role of Digital Twins in Predictive Maintenance and Cyber Resilience
Manojkumar, R., Srinivasarao, N., Konda, A. S., Uppuleti, K. P., Srinivas, T. A. S., & Srinivasulu, G. (2026) · IGI Global eBooks · doi:10.4018/979-8-2600-1498-1.ch001
Reis, L. P., Fernandes, J. M., & de Campos, S. (2026) · The TQM Journal · doi:10.1108/tqm-08-2025-0456
Chen, D., Gong, Y., Chen, J., & He, R. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.117578
A Merging-Assembly Strategy for Complex Scenario-Oriented Digital Twin Modeling
Guo, H., Liu, X., Wang, C., Wang, F., & Hui, C. (2026) · Digital engineering. · doi:10.1016/j.dte.2026.100117
Cognitive Assistants for Integrated System/Software Engineering
Rosenberg, D., & Madni, A. M. (2026) · Conference on systems engineering research series · doi:10.1007/978-3-032-12309-1_55
Self-citationStructured Development of Modeling and Simulation Testbed
Ordoukhaniana, E., & Madni, A. M. (2026) · Conference on systems engineering research series · doi:10.1007/978-3-032-12309-1_53
Self-citationExploiting Augmented Intelligence in Realizing and Operating a Digitally Transformed Enterprise
Madni, A. M., & Noguchi, R. A. (2026) · Conference on systems engineering research series · doi:10.1007/978-3-032-12309-1_56
Self-citationKumar, S., Vaishya, S., Rani, S. S., Agarwal, P., Chandra, D., & Tomar, R. S. (2025) · IEEE International Conference on Recent Trends in Computing and Smart Mobility (RCSM) · doi:10.1109/rcsm67767.2025.11506702
Waki, R., Kitano, T., Manabe, T., Higaki, T., & Matsutomo, S. (2026) · IIEEJ International Conference on Image Electronics and Visual Computing (IEVC) · doi:10.1109/ievc69170.2026.11508384
Combining Life Support Systems with Digital Twins: A New Potential?
Leese, F., & Olthoff, C. (2026) · EASN International Conference on Innovation in Aviation & Space Towards Sustainability Today & Tomorrow · doi:10.3390/engproc2026133094
Fundamentals and Architecture of Digital Twins in the Context of Geospatial Data
Kumar, R., Sharma, M., Kumar, S., Kumar, S., & Singh, B. A. (2026) · Data-intensive research · doi:10.1007/978-3-032-20311-3_1
Infrastructure Management and Asset Tracking Using Digital Twins for Geo-Spatial Science
Kumar, R., Singh, B. A., Sharma, M., Kumar, S., & Kumar, S. (2026) · Data-intensive research · doi:10.1007/978-3-032-20311-3_8
Vukmirović, D., Radojičić, S., Stanojević, N. D., Dragović, N., & Comic, T. (2026) · Advanced sciences and technologies for security applications · doi:10.1007/978-3-032-20948-1_5
Introduction to Prototyping: Definitions, Concepts and Enabling Technologies
Kabir, M. R., & Ray, S. (2026) · Synthesis lectures on engineering, science, and technology · doi:10.1007/978-3-032-18938-7_1
MBSE-Based Multi-Domain Collaborative Design Method for Cross-Belt Sorters
Qu, Y., Zhao, N., & Zhang, H. (2026) · Digital engineering. · doi:10.1016/j.dte.2026.100131
AI-Driven Predictive Maintenance in Industry 4.0: A Systematic Review and Research Directions
Cherradi, H., Benjilali, K., Elasri, A., Hamida, S., Cherradi, B., & Bouattane, O. (2026) · International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) · doi:10.1109/iraset68627.2026.11538626
The economics of smart manufacturing: productivity, costs, and competitive advantage
Hashem, T. N. (2025) · Journal of Production Operations Management and Economics · doi:10.55529/jpome.51.155.164
A review of digital twin methods and applications for bearings under marine operating constraints
Shi, J., Guo, Z., Gu, J., Zhang, Z., Mi, Z., & Du, F. (2026) · Ships and Offshore Structures · doi:10.1080/17445302.2026.2684687
Digital Twin for Safety Validation and Crash Scenario Simulations
Aarthi, S., Ravikumar, R. N., Turaev, D., Ashirova, A. I., Urozboev, K., & Naveena, N. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-7785-8.ch004
Digital Twins Technology and the Relationship with Simulation for Operational Excellence
Zenza, F., Ramos, A. L., Ferreira, L. P., & Ribeiro, R. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-18878-6_29
Methodologies for the Implementation of a Digital Twin
Zenza, F., Ferreira, L. P., Gonçalves, C., Ribeiro, R., & Ramos, A. L. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-18878-6_28
Gall, G. V., & Avasilcăi, S. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-18878-6_25
Ngcukayitobi, M. (2026) · CEAS Aeronautical Journal · doi:10.1007/s13272-026-00985-9
Waheed, H., & Abro, G. E. M. (2026) · Discover Vehicles · doi:10.1007/s44465-026-00025-0 · Cited by 1
Grazianová, M., Hrubovčáková, A., Halaszova, I., & Mésároš, P. (2026) · Buildings · doi:10.3390/buildings16132640
Microgrid Digital Twin: An Overview of Concepts, Applications, and Future Directions
Qasery, M. E., Barkouki, B. E., Laamim, M., Abbou, A., Idkhajine, L., & Rochd, A. (2026) · Global Power, Energy and Communication Conference · doi:10.1109/gpecom70462.2026.11578625
A Theoretical Framework for Requirements Management in Complex Engineering Projects
Vieira, D. R., Vieira, R. K., & Bravo, A. (2026) · Systems · doi:10.3390/systems14070780
Александрович, Ч. В. (2026) · Программные системы и вычислительные методы · doi:10.7256/2454-0714.2026.3.78692
Clarifying digital Twin buzzword: a novel generic evaluation model
Liu, Z., Araghi, S. N., Sarkar, A., & Karray, M. H. (2026) · Digital Twin · doi:10.1080/27525783.2026.2694877 · Cited by 5
Modeling and simulation of cruise ship cooling water system using MBSE methodology and tool
Liu, G., Yan, D., Wu, W., Du, Y., Wen, G., Tan, X., & Yang, L. (2026) · Results in Engineering · doi:10.1016/j.rineng.2026.112030
Kabashkin, I. (2026) · Machine Learning and Knowledge Extraction · doi:10.3390/make8070210
Metadata
- Authors
- Madni, A. M., Madni, C. C., & Lucero, S. D.
- Affiliations
- Intelligent Systems Technology (United States) (US)
- Research and Engineering, Office of the USD, Washington, DC 20301–3030, USA
- Corresponding author
- Madni, Azad M.
- Venue
- Systems
- Publisher
- MDPI AG
- Published
- 2019-01-30
- Volume
- 7
- Issue
- 1
- Pages
- 7
- ISSN
- 2079-8954 (ISSN-L)
- OpenAlex
- W2912589244
- Cited by
- 1,050
- References
- 23
- Citations by year
- 2026: 124 · 2025: 166 · 2024: 189 · 2023: 201 · 2022: 159 · 2021: 121 · 2020: 68 · 2019: 22
- FWCI
- 51.22
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Systems (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Systems Engineering Methodologies and Applications
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Mathematics › Modeling and Simulation › Modeling and Simulation Systems
- Keywords
- digital twin, model-based systems engineering, virtual prototype, system simulation, Internet of Things
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
