Digital Twin—The Simulation Aspect

About The Importance of Autonomy and Digital Twins for the Future of Manufacturing

Rosen, R., von Wichert, G., Lo, G., & Bettenhausen, K. D. (2015) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2015.06.141 · Cited by 1,474

Air Vehicle Integration and Technology Research (AVIATR). Task Order 0003: Condition-Based Maintenance Plus Structural Integrity (CBM+SI) Demonstration (April 2011 to August 2011)

Fitzwater, L. M., Torng, Y. T., & Halbert, K. (2011) · doi:10.21236/ada586666 · Cited by 1

The Role of Digital Twins in Collaborative Cyber-Physical Systems

Nazarenko, A., & Camarinha-Matos, L. (2020) · Doctoral Conference on Computing, Electrical and Industrial Systems · doi:10.1007/978-3-030-45124-0_18 · Cited by 21

Using a Cyber Digital Twin for Continuous Automotive Security Requirements Verification

da Silva, A. C. F., Wagner, S., Lazebnik, E., & Traitel, E. (2022) · IEEE Software · doi:10.1109/ms.2022.3171305 · Cited by 21

A Case Study of Digital Twin for a Manufacturing Process Involving Human Interactions

Latif, H., Shao, G.-D., & Starly, B. (2020) · Online World Conference on Soft Computing in Industrial Applications · doi:10.1109/wsc48552.2020.9383897 · Cited by 15

A Cognitive Approach to Manage the Complexity of Digital Twin Systems

Lu, J., Zheng, X., Schweiger, L., & Kiritsis, D. (2021) · Progress in IS · doi:10.1007/978-3-030-72090-2_10 · Cited by 22

Unifying Digital Twin Framework: Simulation-Based Proof-of-Concept

Traoré, M. (2021) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2021.08.105 · Cited by 15

Digital twin for next gen concretes: On-demand tuning of vulnerable mixtures through Explainable and Anomalous Machine Learning

Naser, M. Z. (2022) · Cement and Concrete Composites · doi:10.1016/j.cemconcomp.2022.104640 · Cited by 23

Hybrid teams of industry 4.0: A work place considering robots as key players

Shehadeh, M. A., Stefan, S., Richert, A., & Jeschke, S. (2017) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2017.8122777 · Cited by 21

Methods of computational modeling of coronary heart vessels for its digital twin

Naplekov, I., Zheleznikov, I., Pashchenko, D., Kobysheva, P., Moskvitina, A., Mustafin, R. M., Gnutikova, M., Mullagalieva, A., & Uzlov, P. (2018) · MATEC Web of Conferences · doi:10.1051/matecconf/201817201009 · Cited by 21

Concept, Creation, Services and Future Directions of Digital Twins in the Construction Industry: A Systematic Literature Review

Liu, J., Duan, L., Lin, S., Ji, M., & Zhao, J. (2024) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-024-10140-4 · Cited by 22

Parallel simulation and prediction techniques for digital twins in urban underground spaces

Gong, H., Su, D., Zeng, S., & Chen, X. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106212 · Cited by 21

A comprehensive review of extended reality and its application in aerospace engineering

Rostami, M., Pradhan, P., Karki, N., Omorodion, J., Milani, P., Kamoonpuri, J. M., Liu, H. Y., & Chung, J. (2025) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2025.101118 · Cited by 22

Machine-Learning-Based Approach for Parameterizing Material Flow Simulation Models

Vernickel, K., Brunner, L., Hoellthaler, G., Sansivieri, G., Härdtlein, C., Trauner, L., Bank, L., Fischer, J., & Berg, J. (2020) · Procedia CIRP · doi:10.1016/j.procir.2020.04.018 · Cited by 20

Applications of Digital Twins to Offshore Oil/Gas Exploitation: From Visualization to Evaluation

Li, W., Pu, D., Huang, M., & Miao, Q. (2020) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2021.04.166 · Cited by 20

BlockTwins: A Blockchain-Based Digital Twins Framework

Hemdan, E. E., & Sayed, A. (2021) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-65691-1_12 · Cited by 18

MIKADO: a smart city KPIs assessment modeling framework

Sanctis, M. D., Iovino, L., Rossi, M. T., & Wimmer, M. (2021) · Software & Systems Modeling · doi:10.1007/s10270-021-00907-9 · Cited by 20

Research and practice of lightweight digital twin speeding up the implementation of flexible manufacturing systems

Zhang, X., Hu, B., Xiong, G., Liu, X., Dong, X., & Li, D. (2021) · IEEE International Conference on Digital Twins and Parallel Intelligence (DTPI) · doi:10.1109/dtpi52967.2021.9540104 · Cited by 20

A Surrogate Model to Predict Production Performance in Digital Twin-Based Smart Manufacturing

Chua, P. C., Moon, S. K., Ng, Y. T., & Ng, H. Y. (2021) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4053038 · Cited by 20

Failure diagnosis system using a new nonlinear mapping augmentation approach for deep learning algorithm

Kim, D.-Y., Woo, Y.-J., Kang, K., & Yoon, G. H. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2022.108914 · Cited by 21

Simplexity testbed: A model-based digital twin testbed

Wang, T., Tan, C., Huang, L., Shi, Y., Yue, T., & Huang, Z. (2022) · Computers in Industry · doi:10.1016/j.compind.2022.103804 · Cited by 20

Manufacturing crisis and twin-oriented manufacturing

Xiao, W., Qiu, T., Liu, Q., Zhao, G. L., Xing, H., & Li, R. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.02.002 · Cited by 20

Image-based deep learning for smart digital twins: a review

Islam, M. R., Subramaniam, M., & Huang, P.-C. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-024-11002-y · Cited by 25

Reinforcement Learning of Material Flow Control Logic Using Hardware-in-the-Loop Simulation

Jaensch, F., Csiszar, A., Kienzlen, A., & Verl, A. W. (2018) · International Conference on Artificial Intelligence for Industries (AI4I) · doi:10.1109/ai4i.2018.8665712 · Cited by 20

Towards a DevOps Approach in Cyber Physical Production Systems Using Digital Twins

Ugarte, M., Etxeberria, L., & Sagardui, G. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-55583-2_15 · Cited by 21

Partial Least Squares Regression of Oil Sands Processing Variables within Discrete Event Simulation Digital Twin

Wilson, R., Mercier, P. H. J., Patarachao, B., & Navarra, A. (2021) · Minerals · doi:10.3390/min11070689 · Cited by 20

A Digital Twin Approach for Soil Moisture Measurement with Physically Based Rendering Simulations and Machine Learning

Parewai, I., & Köppen, M. (2025) · Electronics · doi:10.3390/electronics14020395 · Cited by 22

Digital Twin for resilience and sustainability assessment of port facility

Zhu, M., Calderón, C., Ford, A. C., Robson, C., & Jin, J.-Y. (2025) · Sustainable and Resilient Infrastructure · doi:10.1080/23789689.2025.2526928 · Cited by 23

Cousins, Siblings and Twins: A Review of the Geological Model’s Place in the Digital Mine

Hodgkinson, J. H., & Elmouttie, M. (2020) · Resources · doi:10.3390/resources9030024 · Cited by 19

Understanding Digital Twins for Cyber-Physical Systems: A Conceptual Model

Yue, T., Arcaini, P., & Ali, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-83723-5_5 · Cited by 19

Digital Twin in Practice: Emergent Insights from an ethnographic-action research study

Agrawal, A., Singh, V., Thiel, R., Pillsbury, M., Knoll, H., Puckett, J., & Fischer, M. (2022) · Construction Research Congress 2022 · doi:10.1061/9780784483961.131 · Cited by 19

Digital Twins-Based Smart Monitoring and Optimisation of Mineral Processing Industry

Hasidi, O., Abdelwahed, E. H., Qazdar, A., Boulaamail, A., Krafi, M., Benzakour, I., Bourzeix, F., Baïna, S., Bäına, K., Cherkaoui, M., et al. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-20490-6_33 · Cited by 21

Digital Twin and Cyber-Physical System Integration in Commercial Vehicles: Latest Concepts, Challenges and Opportunities

de Oliveira, V. F., Matiolli, G., Bordin, C. J., Gaspar, R., & Lins, R. G. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3378579 · Cited by 25

Optimal RUL Estimation: A State-of-Art Digital Twin Application

Anis, M. D., Taghipour, S., & Lee, C.-G. (2020) · Annual Reliability and Maintainability Symposium (RAMS) · doi:10.1109/rams48030.2020.9153669 · Cited by 21

A deep reinforcement learning based digital twin framework for resilient production planning under demand uncertainty and an empirical study in semiconductor wafer fabrication

Kuo, H.-A., Hong, T.-Y., & Chien, C. (2025) · Computers & Industrial Engineering · doi:10.1016/j.cie.2025.111389 · Cited by 23

Application of virtual reality technology and digital twin in digital media communication

Li, M., & Shao, W.-J. (2020) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-189501 · Cited by 14

Towards the implementation of the Digital Twin in CMM inspection process: opportunities, challenges and proposals

Gaha, R., Durupt, A., & Eynard, B. (2021) · Procedia Manufacturing · doi:10.1016/j.promfg.2021.07.033 · Cited by 17

Virtualization of Self-Driving Algorithms by Interoperating Embedded Controllers on a Game Engine for a Digital Twining Autonomous Vehicle

Yun, H., & Park, D. (2021) · Electronics · doi:10.3390/electronics10172102 · Cited by 20

A Digital Twin-Oriented Lightweight Approach for 3D Assemblies

Luo, F., Liu, Q., & Zhang, D. (2021) · Machines · doi:10.3390/machines9100231 · Cited by 18

Machine-Learning-Based Digital Twin in Manufacturing: A Bibliometric Analysis and Evolutionary Overview

Sheuly, S. S., Ahmed, M., & Begum, S. (2022) · Applied Sciences · doi:10.3390/app12136512 · Cited by 15

A skill- and feature-based approach to planning process monitoring in assembly planning

Gonnermann, C., Hashemi-Petroodi, S. E., Thevenin, S., Dolgui, A. B., & Daub, R. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-09931-5 · Cited by 18

Comprehensive Survey of the Landscape of Digital Twin Technologies and Their Diverse Applications

Chen, H., Shao, H., Deng, X., Wang, L., & Wang, X. (2023) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2023.027834 · Cited by 20

Digital Twin Technology in the Gas Industry: A Comparative Simulation Study

Yun, J., Kim, S., & Kim, J. (2024) · Sustainability · doi:10.3390/su16145864 · Cited by 21

A proposed methodology to develop digital twin framework for plasma processing

Mitchell, A., Wei, X., Sun, R., Yamamura, K., Ye, L., Corney, J., & Yu, N. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.103462 · Cited by 22

CITY PROFILE: USING SMART DATA TO CREATE DIGITAL URBAN SPACES

Ma, Y., Li, G., Xie, H., & Zhang, H. (2018) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-iv-4-w7-75-2018 · Cited by 18

Enabling the Smart Home Through a Semantic-Based Context-Aware System

Mahroo, A., Spoladore, D., Caldarola, E. G., Modoni, G. E., & Sacco, M. (2018) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2018.8480414 · Cited by 18

iLabs as an online laboratory platform: A case study at Stanford University during the COVID-19 Pandemic

Zaman, M. A., Neustock, L. T., & Hesselink, L. (2021) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon46332.2021.9454028 · Cited by 18

Computational Experiments for Complex Social Systems—Part II: The Evaluation of Computational Models

Lu, M., Chen, S., Xue, X., Wang, X., Zhang, Y., Zhang, Y., & Wang, F. (2021) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2021.3121323 · Cited by 18

Digital phenotyping and data inheritance

Green, S., & Svendsen, M. N. (2021) · Big Data & Society · doi:10.1177/20539517211036799 · Cited by 18

Strategies for Achieving Pre-emptive Resilience in Military Supply Chains

Sani, S., Schaefer, D., & Milisavljevic-Syed, J. (2022) · Procedia CIRP · doi:10.1016/j.procir.2022.05.186 · Cited by 20

Digital twin simulation modeling process with system dynamics: An application to naval ship operation

Choi, J.-W., Moon, S., & Min, S. (2023) · International Journal of Robust and Nonlinear Control · doi:10.1002/rnc.6893 · Cited by 17

Knowledge Equivalence in Digital Twins of Intelligent Systems

Zhang, N., Rami Bahsoon, A., Tziritas, N., & Theodoropoulos, G. K. (2023) · ACM Transactions on Modeling and Computer Simulation · doi:10.1145/3635306 · Cited by 18

Digital Twins for Cyber-Biophysical Systems: Challenges and Lessons Learned

Dávid, I., Archambault, P., Wolak, Q., Vu, C. V., Lalonde, T., Riaz, K., Syriani, E., & Sahraoui, H. A. (2023) · ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS) · doi:10.1109/models58315.2023.00014 · Cited by 22

Landscaping the digital twin technology: Patent-based networks and technology reference model

Sung, K., Park, K. T., & Lee, H. (2024) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2024.123576 · Cited by 17

A Digital Twin for Unconventional Reservoirs: A Multiscale Modeling and Algorithm to Investigate Complex Mechanisms

Zhang, T., Li, Y., Cai, J., Meng, Q., Sun, S., & Li, C. (2020) · Geofluids · doi:10.1155/2020/8876153 · Cited by 17

Estimating the Characteristic Curve of a Directional Control Valve in a Combined Multibody and Hydraulic System Using an Augmented Discrete Extended Kalman Filter

Khadim, Q., Kiani-Oshtorjani, M., Jaiswal, S., Matikainen, M. K., & Mikkola, A. (2021) · Sensors · doi:10.3390/s21155029 · Cited by 17

Uncertainty quantification in ICME workflows for fatigue critical computational modeling

Whelan, G. F., & McDowell, D. L. (2019) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2019.106673 · Cited by 17

From Data Mirror to Smart Copilot: A Survey on NextG Semantic Communication for Propelling Digital Twin World Into Cognitive Stage

Zhu, F., Chen, J., Wen, J., Yang, Y., Yi, C., Tie, Y., Zhang, P., Cai, J., Niyato, D. T., & Guizani, M. (2026) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2026.3665395 · Cited by 26

Inference of Simulation Models in Digital Twins by Reinforcement Learning

Dávid, I., Galasso, J., & Syriani, E. (2021) · 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C) · doi:10.1109/models-c53483.2021.00038 · Cited by 17

Key-Components for Digital Twin Modeling With Granularity: Use Case Car-as-a-Service

Steinmetz, C., Schroeder, G. N., Rodrigues, R. N., Rettberg, A., & Pereira, C. E. (2021) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2021.3131532 · Cited by 16

A Digital Twin Framework for Environmental Sensing with sUAS

Hollenbeck, D., & Chen, Y. (2022) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-021-01542-8 · Cited by 16

Digital Twins in Industry 5.0: Challenges in Modeling and Communication

Balogh, M., Földvári, A., & Varga, P. (2023) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms56928.2023.10154424 · Cited by 16

NS+NDT: Smart integration of Network Simulation in Network Digital Twin, application to IoT networks

Si-Mohammed, S., Bardou, A., Begin, T., Lassous, I. G., & Primet, P. V.-B. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.03.038 · Cited by 16

Knowledge Graph-Enhanced Digital Twin Framework for Optimized Job Shop Scheduling in Smart Manufacturing

Tarek, N., Algarni, A. D., El-Hefnawy, N., Abdelkader, H. M., & Abdelatey, A. (2025) · IEEE Access · doi:10.1109/access.2025.3532600 · Cited by 17

Digital twin in consumer choice modeling

Vijayakumar, D. S. (2020) · Advances in computers · doi:10.1016/bs.adcom.2019.09.010 · Cited by 16

Formal Properties of the Digital Twin – Implications for Learning, optimization, and Control

Cronrath, C., Ekstrom, L., & Lennartson, B. (2020) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case48305.2020.9216822 · Cited by 16

A Hardware Testbed for Dynamic Data-Driven Aerospace Digital Twins

Salinger, S., Kapteyn, M. G., Kays, C., Pretorius, J. V. R., & Willcox, K. E. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61725-7_7 · Cited by 16

Digital Twin Technology in Wireless Communication: A Comprehensive Engineering Survey

Bhatia, M., & Kumar, R. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3565843 · Cited by 15

Linking digital twin paradigm for urban heat monitoring and policy integration to building smart city climate resilience

Hossain, M. I., Hossan, M. R., Shaon, Z. H., & Ferdous, M. (2026) · Discover Cities · doi:10.1007/s44327-025-00179-8 · Cited by 13

Simulation Components in Gazebo

Peake, I., Delfa, J. L., Bejarano, R., & Blech, J. (2021) · International Conference on Industrial Technology · doi:10.1109/icit46573.2021.9453594 · Cited by 14

Enhanced Subcontractors Allocation for Apartment Construction Project Applying Conceptual 4D Digital Twin Framework

Kim, W.-G., Ham, N., & Kim, J.-J. (2021) · Sustainability · doi:10.3390/su132111784 · Cited by 12

Sustainable Hyperautomation in High-Tech Manufacturing Industries: A Case of Linear Electromechanical Actuators

Fedosovskii, M. E., Уваров, М. М., Андреевич, А. С., Pyrkin, A. A., Colombo, A. W., & Prattichizzo, D. (2022) · IEEE Access · doi:10.1109/access.2022.3205623 · Cited by 15

Development of a digital model and metamodel to improve the performance of an automated manufacturing line

Ruane, P., Walsh, P., & Cosgrove, J. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2022.10.011 · Cited by 18

Cloud-based process design in a digital twin framework with integrated and coupled technology models for blisk milling

Rudel, V., Kienast, P., Vinogradov, G., Ganser, P., & Bergs, T. (2022) · Frontiers in Manufacturing Technology · doi:10.3389/fmtec.2022.1021029 · Cited by 14

A self-evolving network-based artificial society model for the experiment analysis of complex social system

Xuan, Y., Yu, X., Wang, Y., Liu, D., Ma, Q., & Xue, X. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119057 · Cited by 17

Toward a shape-performance integrated digital twin based on hybrid reduced-order modeling for engineering structures

He, X., Lai, X., Li, K., Wang, S., Song, X., & Sun, W. F. (2023) · Engineering With Computers · doi:10.1007/s00366-023-01862-3 · Cited by 15

Forests in the Digital Age: Concepts and Technologies for Designing and Deploying Forest Digital Twins

Döllner, J., Amicis, R. D., Burmeister, J.-M., & Richter, R. (2023) · international conference on 3D web technology · doi:10.1145/3611314.3616067 · Cited by 17

A framework for digital twins solutions for 5 PL operators

Nicoletti, B., & Appolloni, A. (2023) · Technology in Society · doi:10.1016/j.techsoc.2023.102415 · Cited by 17

Mutual Active Learning for Engineering Regulated Statistical Digital Twin Models

Wang, L., Wang, X. V., Ji, Q., Wang, L., & Jin, R. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3344134 · Cited by 15

A Comprehensive Review on Artificial Intelligence-Based Applications for Transformer Thermal Modeling: Background and Perspectives

Souza, J. P. D. C., Picher, P., Zinflou, A., Fofana, I., & Asl, M. B. (2024) · IEEE Access · doi:10.1109/access.2024.3480789 · Cited by 15

Synchronizing Digital Process Twins between virtual products and resources – A virtual design method

Illmer, B., & Vielhaber, M. (2019) · Procedia CIRP · doi:10.1016/j.procir.2019.04.227 · Cited by 15

A Service-based Architecture for the Interaction of Control and MES Systems in Industry 4.0 Environment

Azarmipour, M., Elfaham, H., Gries, C., Kleinert, T., & Epple, U. (2020) · IEEE International Conference on Industrial Informatics (INDIN) · doi:10.1109/indin45582.2020.9442083 · Cited by 15

A DEVS-based engine for building digital quadruplets

Niyonkuru, D., & Wainer, G. (2021) · SIMULATION · doi:10.1177/00375497211003130 · Cited by 15

Modeling a simulated Forest to get Burning Times of Tree Species using a Digital Twin

Sanchez-Guzman, G., Velásquez, W., & Alvarez‐Alvarado, M. S. (2022) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc54503.2022.9720768 · Cited by 16

What is a Digital Twin – A Mediation Approach

Dahmen, U., & Roßmann, J. (2021) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit51626.2021.9491883 · Cited by 15

ASSISTANT: Learning and Robust Decision Support System for Agile Manufacturing Environments

Beldiceanu, N., Dolgui, A. B., Gonnermann, C., Castañé, G. G., Kousi, N., Meyers, B., Prud’homme, J., Thevenin, S., Vyhmeister, E., & Östberg, P.-O. (2021) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2021.08.074 · Cited by 15

Digital Twin-assisted anomaly detection for industrial scenarios

Alcaraz, C., & López, J. (2024) · International Journal of Critical Infrastructure Protection · doi:10.1016/j.ijcip.2024.100721 · Cited by 18

Digital Twins for Circular Economy - Enabling Decision Support for R-Strategies

Fraunhofer, I. P. K., Mügge, J., Hahn, I. R., Fraunhofer, I. P. K., Riedelsheimer, T., Fraunhofer, I. P. K., Chatzis, J., & mbH, T.-I. (2022) · Industrie 4.0 Management · doi:10.30844/im_22-6_42-46 · Cited by 14

Overview and Comparison of Asset Information Model Standards

Miny, T., Thies, M., Lukic, L., Käbisch, S., Oladipupo, K., Diedrich, C., & Kleinert, T. (2023) · IEEE Access · doi:10.1109/access.2023.3312286 · Cited by 14

Enhancing Operational Resilience of Critical Infrastructure Processes Through Chaos Engineering

Dedousis, P., Stergiopoulos, G., Arampatzis, G., & Gritzalis, D. A. (2023) · IEEE Access · doi:10.1109/access.2023.3316028 · Cited by 14

A Method for Calculating Optimum Digital Twin Fidelity

Kober, C., Fette, M., & Wulfsberg, J. P. (2023) · Procedia CIRP · doi:10.1016/j.procir.2023.09.141 · Cited by 16

El Gemelo Digital y su aplicación en la Automática

Chicaiza, W. D., Gómez, J., Sánchez, A. J., & Escaño, J. M. (2024) · Revista Iberoamericana de Automática e Informática Industrial RIAI · doi:10.4995/riai.2024.20175 · Cited by 15

Process Manufacturing Intelligence Empowered by Industrial Metaverse: A Survey

Luo, W., Huang, K., Liang, X., Ren, H., Zhou, N., Zhang, C., Yang, C., & Gui, W. (2024) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2024.3420958 · Cited by 18

Industry 4.0: digital twins characteristics, applications, and challenges in-built environments

Zribi, H., Abid, T. B., Elloumi, A., Hani, Y., Graba, B. B., & Elmhamedi, A. (2025) · Production & Manufacturing Research · doi:10.1080/21693277.2025.2456277 · Cited by 17

Semi-automatic generation of a virtual representation of a production cell

Thongnuch, S., Fay, A., & Drath, R. (2018) · at - Automatisierungstechnik · doi:10.1515/auto-2017-0108 · Cited by 14

3D in-situ simulation and particle tracing of gas filtration process for ultrafine particles removal using a hollow fiber membrane

Lin, F., Chen, P.-C., Huang, T.-C., Lin, G., & Tung, K. (2021) · Journal of Membrane Science · doi:10.1016/j.memsci.2021.119380 · Cited by 14

Enabling automated engineering’s project progress measurement by using data flow models and digital twins

Ebel, H., Riedelsheimer, T., & Stark, R. (2021) · International Journal of Engineering Business Management · doi:10.1177/18479790211033697 · Cited by 14

EXPLORING DIGITAL TWIN ADAPTATION TO THE URBAN ENVIRONMENT: COMPARISON WITH CIM TO AVOID SILO-BASED APPROACHES

Deprêtre, A., Jacquinod, F., & Mielniczek, A. (2022) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-v-4-2022-337-2022 · Cited by 14

Nonequilibrium statistical mechanics and optimal prediction of partially-observed complex systems

Rupe, A., Vesselinov, V. V., & Crutchfield, J. P. (2022) · New Journal of Physics · doi:10.1088/1367-2630/ac95b7 · Cited by 14

Fast electrothermal coupling calculation method for supporting digital twin construction of electrical equipment

Zhu, X., Wang, Y., Meng, R., Shi, L. Q., & Wu, S. (2022) · High Voltage · doi:10.1049/hve2.12260 · Cited by 14

Characterization of ground-to-air emissions with sUAS using a digital twin framework

Hollenbeck, D., & Chen, Y. (2020) · International Conference on Unmanned Aircraft Systems (ICUAS) · doi:10.1109/icuas48674.2020.9213959 · Cited by 14

Artificial Intelligence-Aided Digital Twin Design: A Systematic ReviewPreprint

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