Digital twin modeling

TensorFlow: A system for large-scale machine learningPreprint

Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,548

Digital Twin in Industry: State-of-the-Art

Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2873186 · Cited by 4,391

Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems

Grieves, M., & Vickers, J. (2017) · Transdisciplinary Perspectives on Complex Systems · doi:10.1007/978-3-319-38756-7_4 · Cited by 3,727

Digital twin: values, challenges and enablers from a modeling perspective

Rasheed, A., San, O., & Kvamsdal, T. (2020) · IEEE Access · doi:10.1109/access.2020.2970143 · Cited by 1,952

Data-driven smart manufacturing

Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2018.01.006 · Cited by 1,934

Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues

Lu, Y., Liu, C., Wang, K. I., Huang, H., & Xu, X. W. (2019) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2019.101837 · Cited by 1,753

Enabling technologies and tools for digital twin

Qi, Q., Tao, F., Hu, T., Anwer, N., Liu, A., Wei, Y., Wang, L., & Nee, A. Y. C. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2019.10.001 · Cited by 1,579

A survey on digital twin: definitions, characteristics, applications, and design implications

Barricelli, B. R., Casiraghi, E., & Fogli, D. (2019) · IEEE Access · doi:10.1109/access.2019.2953499 · Cited by 1,480

The ‘Digital Twin’ to enable the vision of precision cardiology

Acero, J. C., Margara, F., Marciniak, M., Rodero, C., Lončarić, F., Feng, Y., Gilbert, A., Fernandes, J. F., Bukhari, S. H. A., Wajdan, A., et al. (2020) · European Heart Journal · doi:10.1093/eurheartj/ehaa159 · Cited by 987

Digital Twin for maintenance: A literature review

Errandonea, I., Beltrán, S., & Arrizabalaga, S. (2020) · Computers in Industry · doi:10.1016/j.compind.2020.103316 · Cited by 819

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

Digital twins enable shipbuilding

Liu, J., Zhang, Y., Liu, Z., Leng, J., Zhou, H.-G., Gu, S., & Liu, X.-J. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.09.007 · Cited by 15

Scalable Data Transformation Models for Physics-Informed Neural Networks (PINNs) in Digital Twin-Enabled Prognostics and Health Management (PHM) Applications

Barimah, A. K., Onu, O. P., Niculita, O., Cowell, A., & McGlinchey, D. (2025) · Computers · doi:10.3390/computers14040121 · Cited by 20

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-based multi-objective optimized task offloading and scheduling scheme for vehicular edge networks

Zhu, L., Li, B., & Tan, L. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.107517 · Cited by 17

Continuous Evolution of Digital Twins using the DarTwin Notation

Mertens, J., Klikovits, S., Bordeleau, F., Denil, J., & Haugen, Ø. (2024) · Journal of Software and Systems Modeling · doi:10.1007/s10270-024-01216-7 · Cited by 24

Data Generation Approach Based on Data Model Fusion: An Application for Rolling Bearings Fault Diagnosis With Small Samples

Zhu, Y., Cheng, J., Liu, Z., Zou, X., Cheng, Q., Xu, H., Wang, Y., & Tao, F. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3504567 · Cited by 17

A fidelity evaluation method for digital twin model of aero-engine assembly characteristics

Zhang, Y., Sun, H., Zhang, X., & Liu, W. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.12.012 · Cited by 17

Digital twin based predictive diagnosis approach for submarine suspended pipelines

Chen, F., Wei, H., Tang, J., Sun, W., Zhao, X., Li, Y., Dong, S., Zhang, H., & Liu, G. (2025) · International Journal of Pressure Vessels and Piping · doi:10.1016/j.ijpvp.2025.105451 · Cited by 17

In-process surface quality monitoring of the slender workpiece machining with digital twin approach

Lu, K., Li, Z., & Longstaff, A. P. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02353-y · Cited by 16

Digital-Twin Predictive Control of Nonlinear Systems With Time Delays, Unknown Dynamics, and Communication Delays

Liu, G. P. (2024) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2024.3471608 · Cited by 16

Real-time drilling torque prediction ahead of the bit with just-in-time learning

Bai, K., Sheng, M., Zhang, H., Fan, H., & Pan, S.-W. (2024) · Petroleum Science · doi:10.1016/j.petsci.2024.12.014 · Cited by 16

Online fatigue life prediction method of jacket structures based on proper orthogonal decomposition and rainflow counting algorithm

Leng, J., Zhao, L., & Mao, H. (2025) · Structures · doi:10.1016/j.istruc.2025.108517 · Cited by 16

Digital twins: digitalization of automated container terminals in seaports

Cao, Y., Zeng, Q., Haralambides, H. E., Wang, Z., & Yang, A. (2025) · Maritime Economics & Logistics · doi:10.1057/s41278-025-00315-6 · Cited by 16

Digital Twin With Soft Actor-Critic Reinforcement Learning for Transitioning From Industry 4.0 to 5.0

Asmat, H., Din, I. U., Almogren, A., & Khan, M. Y. (2025) · IEEE Access · doi:10.1109/access.2025.3546085 · Cited by 17

Digital twin for zinc roaster furnace based on knowledge-guided variable-mass thermodynamics: Modeling and application

Wang, C., Wang, Z., Huang, K., Li, Y., & Yang, C. (2023) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2023.03.001 · 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

Research on Digital Twin Vehicle Stability Monitoring System Based on Side Slip Angle

Wang, J., Zhang, C., Yang, Z., Dang, M., Gao, P., & Feng, Y. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3296268 · Cited by 16

Digital Twin Model and Its Establishment Method for Steel Structure Construction Processes

Liu, Z., & Lin, S. (2024) · Buildings · doi:10.3390/buildings14041043 · Cited by 16

Graphics-based modular digital twin software framework for production lines

Yu, X., Sun, X., & Ou, L. (2024) · Computers & Industrial Engineering · doi:10.1016/j.cie.2024.110308 · Cited by 16

A digital twin approach for weld penetration prediction of tig welding with dual ellipsoid heat source

Qu, H., Chen, J.-H., & Cai, Y. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02431-1 · Cited by 16

Plant cell wall enzymatic deconstruction: Bridging the gap between micro and nano scales

Refahi, Y., Zoghlami, A., Viné, T., Terryn, C., & Paës, G. (2024) · Bioresource Technology · doi:10.1016/j.biortech.2024.131551 · Cited by 16

A generic digital twin model construction strategy for cross-field implementations with comprehensiveness, operability and scalability

Liu, X., Wang, C., Wang, F., Xiao-li, Q., Feng, F., & Sun, Y. (2025) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2025.02.020 · Cited by 16

Tool Wear Monitoring in Micro-Milling Based on Digital Twin Technology with an Extended Kalman Filter

Christiand, C., Kiswanto, G., Baskoro, A. S., Hasymi, Z., & Ko, T. J. (2024) · Journal of Manufacturing and Materials Processing · doi:10.3390/jmmp8030108 · Cited by 18

Machine learning-enhanced digital twins for predictive analytics in battery pack assembly

Waseem, M., Tan, C., Oh, S., Arinez, J. F., & Chang, Q. (2025) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2025.03.007 · Cited by 15

Digital twin modeling Enabling structure full field data reconstruction by Variable fidelity data fusion

Chen, H., Chen, J., Li, Z., Liu, Y., & Wang, J. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.112619 · Cited by 15

Uncertainty-aware digital twins for deep excavations with lateral support

Tian, H.-M., Wang, Y., & Huang, Y. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106658 · Cited by 15

TwinArch: A digital twin reference architecture

Somma, A., Amalfitano, D., Benedictis, A. D., & Pelliccione, P. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2025.112613 · Cited by 19

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

A Framework for Service-Oriented Digital Twin Systems for Discrete Workshops and Its Practical Case Study

Zhang, Q., Wei, Y., Liu, Z., Duan, J., & Qin, J. (2023) · Systems · doi:10.3390/systems11030156 · Cited by 15

Digital Triplet: A Sequential Methodology for Digital Twin Learning

Zhang, X., Lin, D. K. J., & Wang, L. (2023) · Mathematics · doi:10.3390/math11122661 · Cited by 16

An Orchestration Method for Integrated Multi-Disciplinary Simulation in Digital Twin Applications

Brusa, E. G. M., Dagna, A., Delprete, C., & Gentile, R. (2023) · Aerospace · doi:10.3390/aerospace10070601 · Cited by 15

Digital Process Twins as Intelligent Design Technology for Engineering Metaverse/XR Applications

Stary, C. (2023) · Sustainability · doi:10.3390/su152216062 · Cited by 18

Design a low-frequency vibration energy harvesting device based on a square spiral beam local resonant phononic crystal

Chai, Z., Zheng, T., & Xiang, J. (2024) · Physica B Condensed Matter · doi:10.1016/j.physb.2024.415792 · Cited by 15

Digital twin enhanced quality prediction method of powder compaction process

Zuo, Y. N., You, H., Zou, X., Ji, W., & Tao, F. (2024) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2024.102762 · Cited by 15

Smart scheduling of hanging workshop via digital twin and deep reinforcement learning

Pan, J., Zhong, R., Hu, B., Feng, Y., Zhang, Z., & Tan, J. (2024) · Flexible Services and Manufacturing Journal · doi:10.1007/s10696-024-09543-z · Cited by 15

Research on Key Technology of Wind Turbine Drive Train Fault Diagnosis System Based on Digital Twin

Liu, H., Sun, W., Bao, S., Xiao, L., & Jiang, L. (2024) · Applied Sciences · doi:10.3390/app14145991 · Cited by 15

A framework for enhancing cyber incident response with Security-Enhancing Digital Twins in Cyber–Physical Systems

Suhail, S., Iqbal, M., McLaughlin, K., Lee, B., & Imtiaz, M. B. (2025) · Internet of Things · doi:10.1016/j.iot.2025.101547 · Cited by 20

Ten questions concerning Large Language Models (LLMs) for building applications

Ma, N., Labib, R., Amor, R., Chong, A., Fan, C., Forth, K., Fu, X., Fuchs, S., Hong, T., Klimenkova, N., et al. (2026) · Building and Environment · doi:10.1016/j.buildenv.2026.114260 · Cited by 17

Factory Simulation of Optimization Techniques Based on Deep Reinforcement Learning for Storage Devices

Lim, J., & Jeong, J. (2023) · Applied Sciences · doi:10.3390/app13179690 · Cited by 14

A Modeling Approach for Composed Digital Twins in Cooperative Systems

Gil, S., Mikkelsen, P. H., Tola, D., Schou, C., & Larsen, P. G. (2023) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa54631.2023.10275601 · Cited by 15

Implementing Digital Twin and Asset Administration Shell Models for a Simulated Sorting Production System

Ye, X., Xu, W., Liu, J., Zhong, Y., Liu, Q., Zhou, Z., Song, W. S., & Hong, S. H. (2023) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2023.10.600 · Cited by 15

Digital Twins for Discrete Manufacturing Lines: A Review

Feng, X., & Wan, J. (2024) · Big Data and Cognitive Computing · doi:10.3390/bdcc8050045 · Cited by 17

Novel designs of blade mixer impellers from the discrete element method and topology optimization

Castro, M. T., & Ocon, J. D. (2024) · Chemical Engineering Journal · doi:10.1016/j.cej.2024.151863 · Cited by 14

Design and analysis methods for aerostatic bearings: The past, the present, and the future

Gou, N., Cheng, K., & Huo, D. (2024) · Proceedings of the Institution of Mechanical Engineers Part J Journal of Engineering Tribology · doi:10.1177/13506501241250121 · Cited by 14

A meta-PINN framework for online operational monitoring of high-power induction furnace

Zhang, Z., Xu, X., Mao, W., & Li, S. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.07.002 · Cited by 14

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

Digital twin dynamic force-thermal physics sub-cell for CFRP drilling process

Chen, J., An, Q., Chen, M., Ko, T. J., & Chen, T. (2024) · Composites Part B Engineering · doi:10.1016/j.compositesb.2024.112057 · Cited by 14

Construction 4.0: enhancing sustainable construction practices by evaluating digital twin barriers in the Nigerian AEC industry

Ibrahim, K., Okanlawon, T. T., Oyewobi, L. O., Badamasi, A. A., Dodo, M., & Jimoh, R. A. (2024) · Engineering Construction & Architectural Management · doi:10.1108/ecam-04-2024-0520 · Cited by 16

Rate-Splitting Multiple Access Enabled Green Probabilistic Semantic Communication Over Wireless Networks

Xu, R., Yang, Z., Mao, Y., Huang, C., Yang, Q., Xu, L., Xu, W., & Zhang, Z. (2025) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2025.3528943 · Cited by 16

Digital Twin and Artificial Intelligence in Machining: A Bibliometric Analysis

Dambatta, Y. S., Li, Q., Li, B., Zhang, Y., Zhang, B., Liu, D., Zhang, W., Zhou, Z., Feng, Y., Bie, Q., et al. (2025) · Intelligent and sustainable manufacturing · doi:10.70322/ism.2025.10005 · Cited by 15

A methodology for estimating the cost of a digital twin

Su, S., Nassehi, A., McClenaghan, A., Langridge, A., & Hicks, B. J. (2025) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2025.04.004 · Cited by 14

Towards enabling reliable immersive teleoperation through Digital Twin: A UAV command and control use case

Sehad, N., Tu, X.-Y., Rajasekaran, A., Hellaoui, H., Jäntti, R., & Debbah, M. (2023) · Global Communications Conference · doi:10.1109/globecom54140.2023.10437527 · Cited by 14

Anomaly detection based on data super-resolution in industrial cyber–physical systems with multirate sampling

Du, X., Zhou, C., Tian, Y.-C., & Wang, K. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3384275 · Cited by 13

Digital Twin Technology-Based Networking Solution in Low Earth Orbit Satellite Constellations

He, C., Zhang, Y., Ke, J., Yao, M., & Chen, C. (2024) · Electronics · doi:10.3390/electronics13071260 · Cited by 13

Physics-informed DEEPLSTM for digital twin modeling of steam turbine full-operating condition

Li, A., Guan, F., Hu, H., Liu, Z., Xiao, B., Huang, W., & Yang, Y. (2025) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2025.127439 · Cited by 13

Advances in digital twinning for bearing fault diagnosis: a literature review

He, D., Dai, C., Wu, J., Zhuang, Y., & Jin, Z. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/adfaf8 · Cited by 14

From insight to action: Embodied multi-agent system integrating vision language model for digital twin-assisted human-robot collaborative assembly

Liu, C., Qian, Y., Tang, D., Zhu, H., Pang, J., & Cai, Q. (2026) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2026.02.013 · Cited by 13

Digital twin on concepts, enabling technologies, and applications

Bing, Z., Enyan, M., Amu‐Darko, J. N. O., Issaka, E., Liu, H., Junsen, R., & Zhang, X. (2024) · Journal of the Brazilian Society of Mechanical Sciences and Engineering · doi:10.1007/s40430-024-04973-0 · Cited by 15

Meta-inventory management decisions: A theoretical model

Xiao, J., Ma, S., Wang, S., & Huang, G. Q. (2024) · International Journal of Production Economics · doi:10.1016/j.ijpe.2024.109339 · Cited by 13

Toward a systematic reporting framework for Digital Twins: a cooperative robotics case study

Gil, S., Oakes, B. J., Gomes, C., Frasheri, M., & Larsen, P. G. (2024) · International Conference on Advances in System Simulation · doi:10.1177/00375497241261406 · Cited by 18

Digital-Triplet: a new three entities digital-twin paradigm for equipment fault diagnosis

Zhang, H., Wang, Z., Zhang, S., Qiu, L., Wang, Y., Xiang, F., Pan, Z., Zhu, L., & Tan, J. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02471-7 · Cited by 13

A digital twin dynamic migration method for industrial mobile robots

Wang, Y., & Zhao, X. (2024) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2024.102864 · Cited by 13

An architecture for coupled digital twins with semantic lifting

Gil, S., Kamburjan, E., Talasila, P., & Larsen, P. G. (2024) · Journal of Software and Systems Modeling · doi:10.1007/s10270-024-01221-w · Cited by 22

Automation in Unstructured Production Environments Using Isaac Sim: A Flexible Framework for Dynamic Robot Adaptability

Nambiar, S., Jonsson, M., & Tarkian, M. (2024) · Procedia CIRP · doi:10.1016/j.procir.2024.10.173 · Cited by 14

Reflecting city digital twins (CDTs) for sustainable urban development: Roles, challenges and directions

Wang, Q.-C., Sun, M., Liu, X., Tao, F., Yang, D., & Bardhan, R. (2025) · Digital engineering. · doi:10.1016/j.dte.2025.100035 · Cited by 15

Human–AI interactive optimized shared control

Tan, J., Xue, S., Cao, H., & Ge, S. S. (2025) · Journal of Automation and Intelligence · doi:10.1016/j.jai.2025.01.001 · Cited by 14

Digital Twin-driven Inversion of Assembly Precision for Industrial Equipment: Challenges, Progress and Perspectives

Cheng, D., Hu, B., Feng, Y., Yang, J., Zhong, R., Wang, T., & Tan, J. (2025) · Chinese Journal of Mechanical Engineering · doi:10.1186/s10033-025-01224-8 · Cited by 13

Enhancing Safety and Privacy in Industry 4.0: The ICE Laboratory Case Study

Cunico, F., Aldegheri, S., Avogaro, A., Boldo, M., Bombieri, N., Capogrosso, L., Caputo, A., Damiano Carra, B., Centomo, S., Cheng, D. S., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3479411 · Cited by 18

Data-driven digital twin framework for large-scale dynamic structures based on model reduction and damage regression identification

Yang, H., Yan, B., Wu, K., Gao, Y., Deng, H., Lv, Z., & Zhang, B. (2025) · Engineering Structures · doi:10.1016/j.engstruct.2025.119688 · Cited by 12

State-of-art review on load identification and response reconstruction to realize digital twin of infrastructures

Li, Y., Luo, L., Wang, X., Zhou, X., Sun, L., & Xia, Y. (2025) · Advances in Structural Engineering · doi:10.1177/13694332251321196 · Cited by 13

Digital twin embodied interactions design: Synchronized and aligned physical sensation in location-based social VR

Zhang, J., Ma, F., Pi, Y., Du, H., & Pan, X. (2025) · Frontiers in Virtual Reality · doi:10.3389/frvir.2025.1499845 · Cited by 16

Remaining useful life prediction method of centrifugal pump rolling bearings based on digital twins

Zhou, S., Zhang, L., Yang, X., Luo, R., Du, B., & Zeng, W. (2025) · Scientific Reports · doi:10.1038/s41598-025-03952-2 · Cited by 12

Cross-domain comparative analysis of digital twins and universalised solutions

Xiong, G., Li, H., & Gao, Y. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103726 · Cited by 14

Digital twin models for architectural heritage conservation

Guo, M., Shang, X., Zhao, J., Shi, Y. J., Sun, M., Huang, M. D., Wang, G., & Liao, L. (2025) · Journal of Building Engineering · doi:10.1016/j.jobe.2025.113792 · Cited by 12

RadioMamba: Breaking the Accuracy-Efficiency Trade-Off in Radio Map Construction via a Hybrid Mamba-UNet

Jia, H., Cheng, N., Wang, X., Zhou, C., Sun, R., & Shen, X. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3617102 · Cited by 12

A novel physics-guided approach for time-varying mesh stiffness estimation and data generation in gear fault diagnosis under imbalanced data

Yao, J., He, Q., & Peng, Z. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103963 · Cited by 14

The Impact of Automation on the Efficiency of Port Container Terminals

Tsagkaris, P., & Moschovou, T. P. (2025) · Future Transportation · doi:10.3390/futuretransp5040155 · Cited by 12

A review on rail remaining useful life prediction

Cao, Y., Li, J., Sun, Y., Su, S., & Wang, F. (2025) · Transportation Safety and Environment · doi:10.1093/tse/tdaf062 · Cited by 12

Cloud, Edge, and Digital Twin Architectures for Condition Monitoring of Computer Numerical Control Machine Tools: A Systematic Review

Hamza, M. F. (2026) · Information · doi:10.3390/info17020153 · Cited by 12

A Novel Method of Digital Twin-Based Manufacturing Process State Modeling and Incremental Anomaly Detection

Zhang, Q., Liu, Z., Duan, J., & Qin, J. (2023) · Machines · doi:10.3390/machines11020151 · Cited by 14

Development of a Digital Twin Pipeline for Interactive Scientific Simulation and Mixed Reality Visualization

Shin, J.-H., Park, S., Kim, M., Lee, M.-J., Lim, S.-C., & Cho, K.-W. (2023) · IEEE Access · doi:10.1109/access.2023.3314793 · Cited by 12

Interactive Digital Twins for Online Planning and Worker Safety in Intralogistics and Production

Luxenburger, A., Mohr, J., Merkel, D., Knoch, S., Porta, D., Paul, C., Widenka, J., Schäfers, P., Baumann, M., Lehnhoff, S., et al. (2024) · IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR) · doi:10.1109/aixvr59861.2024.00016 · Cited by 12

The effect of industrial solution services (ISS) on innovation performance: The moderating role of sustainable development goals (SDGs)

Basáez, M. O., Bustinza, Ó. F., & Fernández, L. M. M. (2024) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2024.142265 · Cited by 12

Developing Industry 4.0-based e-waste management: a decision-aided tool equipped with continuous function-valued intuitionistic fuzzy sets

Aydoğan, B., Özçelik, G., & Ünver, M. (2024) · International Journal of Environmental Science and Technology · doi:10.1007/s13762-024-05977-y · Cited by 12

An adaptable Digital Twin model for manufacturing

Huang, H., Ji, T., & Xu, X. W. (2024) · Manufacturing Letters · doi:10.1016/j.mfglet.2024.09.142 · Cited by 12

Expert opinion aggregation-based decision support for human-robot collaboration digital twin maturity assessment

Liu, X., Li, G., Xiang, F., Tao, B., & Jiang, G. (2024) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2024.100710 · Cited by 12

The crane radar: Development and deployment of an operational eco-digital twin

de Koning, K. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2024.102938 · Cited by 12

Exploring the Sustainability Benefits of Digital Twin Technology in Achieving Resilient Smart Cities During Strong Earthquake Events

Habib, A., Habib, M., Bashir, B., & Bachir, H. (2025) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-025-10017-z · Cited by 16

Analysis of Machine Learning Algorithms for the Computer Simulation of Moisture Sorption Isotherms of Coffee Beans

Collazos-Escobar, G. A., Gutiérrez-Guzmán, N., Váquiro, H. A., Garcia-Perez, J. V., & Cárcel, J. A. (2025) · Food and Bioprocess Technology · doi:10.1007/s11947-025-03785-x · Cited by 12

Accurate Calculation of Upper Biomass Volume of Single Trees Using Matrixial Representation of LiDAR Data

Kurdi, F. T., Lewandowicz, E., Gharineiat, Z., & Shan, J. (2024) · Remote Sensing · doi:10.3390/rs16122220 · Cited by 12

A deep encoder-decoder for surrogate modelling of liquid moulding of composites

Fernández-León, J., Keramati, K., Miguel, C., González, C., & Baumela, L. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.105945 · Cited by 12

Using a Digital Twin as the Objective Function for Evolutionary Algorithm Applications in Large Scale Industrial Processes

Eklund, M., Sierla, S., Niemistö, H., Korvola, T., Savolainen, J., & Karhela, T. A. (2023) · IEEE Access · doi:10.1109/access.2023.3254896 · Cited by 11

Forest Digital Twin: A Digital Transformation Approach for Monitoring Greenhouse Gas Emissions

Silva, J. R., Artaxo, P. E., & Vital, E. (2023) · Polytechnica · doi:10.1007/s41050-023-00041-z · Cited by 11

A Hybrid Neural Ordinary Differential Equation Based Digital Twin Modeling and Online Diagnosis for an Industrial Cooling Fan

Peng, C.-C., & Chen, Y.-H. (2023) · Future Internet · doi:10.3390/fi15090302 · Cited by 11

Digital twin modeling method of the temperature field of thermo-compression bonding blade based on generative adversarial networks

Deng, Z., Huang, H., Yang, J., Chen, J., Gao, X., & Yang, K. (2023) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2023.103569 · Cited by 12

Research Progress in Modeling and Evaluation of Cooperative Operation System-of-Systems for Manned–Unmanned Aerial Vehicles

Wang, X., Cao, Y., Ding, M., Wang, X., Yu, W., & Guo, B. (2023) · IEEE Aerospace and Electronic Systems Magazine · doi:10.1109/maes.2023.3347504 · Cited by 11

A Data-Driven Digital Twin Architecture for Failure Prediction of Customized Automatic Transverse Robot

Ye, W., Liu, X., Zhao, X., Fu, H., Cai, Y., & Li, H. (2024) · IEEE Access · doi:10.1109/access.2024.3393027 · Cited by 11

Probing a point cloud based expeditious approach with deep learning for constructing digital twin models in shopfloor

Zhao, Z. M., Zhang, Z., Nie, Q., Liu, C., Zhu, H., Chen, K., & Tang, D. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102748 · Cited by 10

Revolutionising agriculture for food security and environmental sustainability: A perspective on the role of digital twin technology

Ametefe, D. S., Hussin, N., John, D., Ametefe, G. D., Aliu, A. A., & Ali, Z. A. (2024) · CABI Reviews · doi:10.1079/cabireviews.2024.0036 · Cited by 12

A systematic review of digital twin about physical entities, virtual models, twin data, and applications

Liu, X., Jiang, D., Tao, B., Xiang, F., Jiang, G., Sun, Y., Kong, J., & Li, G. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.101876 · Cited by 369

Digital Twins: Modeling Hierarchy and Basic Approaches

Liu, X., Liu, J., & Yin, S. (2025) · Springer eBooks · doi:10.1007/978-981-96-9108-1_2

Industry application of digital twin: from concept to implementation

Fang, X., Wang, H., Liu, G., Tian, X., Ding, G., & Zhang, H. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-09632-z · Cited by 135

A Comprehensive Review of Digital Twin from the Perspective of Total Process: Data, Models, Networks and Applications

Wu, H., Ji, P., Ma, H., & Xing, L. (2023) · Sensors · doi:10.3390/s23198306 · Cited by 119

On the Notion of Digital Twins: A Modeling Perspective

Teki̇nerdoğan, B. (2022) · Systems · doi:10.3390/systems11010015 · Cited by 42