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

Rapid digital twin modeling method and system for equipment

Chen, L., Zhou, M., Zhong, J., Lu, Y., & Zheng, Y. (2025) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-025-02699-x · Cited by 3

Optimal Sensor Placement for heat transfer digital twin with Denoising Autoencoder and discrete optimization

Yeo, C., Lee, J.-W., Kim, J., & Kim, Y.-H. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.104139 · Cited by 3

Radio Propagation as a Service: Raytracing-Based Channel Simulation from Camera Data

Sharifipour, S., Määttä, T., Vaara, N., Sangi, P., Huynh, L., Mustaniemi, J., Heikkilä, J., Pessoa, L. M., Teixeira, F. B., & López, M. B. (2025) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco63237.2025.11226811 · Cited by 3

Real-time prediction method of stress responses of jacket platforms under wind-wave-current coupled loads based on reduced-order modeling

Pei, J., Leng, J., Sha, J., Feng, H., & Wang, Y. (2026) · Ocean Engineering · doi:10.1016/j.oceaneng.2025.124146 · Cited by 3

Toward Smart and Green Energy Stations: A Cognitive Digital Twin Framework for Intelligent Management

Zhang, J., Sun, J., Zhao, H., Huang, F., Xu, B., & Wang, Q. (2026) · IEEE Internet Computing · doi:10.1109/mic.2025.3649567 · Cited by 3

Quality-centric digital twins in additive manufacturing: a review of the state-of-the-art, challenges, and future directions

Ehteshamfar, M. V., Xu, X. W., & Yang, S. L. (2026) · Digital Twin · doi:10.1080/27525783.2026.2613469 · Cited by 3

System and system-of-systems digital twins for predictive maintenance and root cause analysis

Hassan, M., & Björsell, N. (2026) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-025-02789-w · Cited by 3

Digital Twin-Enabled Edge Federated Learning for Data Streams

Xie, Z., Pang, J., Huang, Y., Balogun, O., Yu, D., & Cai, Z. (2026) · Tsinghua Science & Technology · doi:10.26599/tst.2024.9010227 · Cited by 3

Behavioral trust management in Digital Twin systems using safe state analysis and virtual validation

Azzedin, F., Alhazmi, T. A., Abawajy, J., & Hammoudeh, M. (2026) · Information and Software Technology · doi:10.1016/j.infsof.2026.108093 · Cited by 3

Attention-Driven Multisensor Fusion for Accurate and Real-Time Wind System Fault Diagnosis

Nakti, I., Mansouri, M. M., Attouri, K., Khedher, A., & Tankari, M. A. (2026) · IEEE Open Journal of the Industrial Electronics Society · doi:10.1109/ojies.2026.3667689 · Cited by 3

Data-driven optical quality inspection and optimization within automated manufacturing processes

Ghosh, T., & Dan, P. K. (2026) · Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture · doi:10.1177/09544054261423498 · Cited by 3

A multidimensional digital twin framework for mine water-sand mixture inrush prevention and control: collaborative multi-physical entities and multi-source data-driven intelligent governance

Wu, B., Sui, W.-H., & Han, G.-L. (2026) · Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards · doi:10.1080/17499518.2026.2649027 · Cited by 2

Enhancing Flexible Healthcare Management Through the Adoption of Digital Twin Technology: An Integrated UTAUT2-TOE Framework with SEM-ANN Analysis

Hossain, I., Belal, H. M., Ratul, S. H., & Rahman, M. A. (2026) · Global Journal of Flexible Systems Management · doi:10.1007/s40171-026-00491-1 · Cited by 3

DP-Twin: A Data–Physics-Driven Method for Modeling Physical Twins of Industrial Cyber-Physical Systems

Mao, J., Sun, Y., li, M., Liu, H., & Tian, Z. (2026) · IEEE Transactions on Industrial Cyber-Physical Systems · doi:10.1109/ticps.2026.3679859 · Cited by 3

Deep learning-assisted digital twin framework for offline fatigue damage prediction of orthotropic steel deck

Lao, W., Xu, Y. L., Zhang, Q., Cui, C., & Ye, Y. (2026) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2026.109676 · Cited by 3

Designing a BIM-enhanced digital twin architecture for construction pollution management in building renovation projects

Pham, T.-A., Ioannou, P. G., & Likhitruangsilp, V. (2026) · International Journal of Construction Management · doi:10.1080/15623599.2026.2651946 · Cited by 4

Digital twin (DT) for deep-sea mining: system design, application and sea trial validation

Liu, W. F., Liu, J., Sun, Y., Li, L., Wang, S. Q., Hong, X., Yu, L., Li, Y., Gu, H., Lianjin, J., et al. (2026) · Ocean Engineering · doi:10.1016/j.oceaneng.2026.125853 · Cited by 3

Digital twin construction for biomimetic materials

Shen, T., Zhang, C., Jiang, L., & Dong, Z. (2026) · Advanced bionics. · doi:10.1016/j.abs.2026.07.006 · Cited by 3

The Benefits of Continuous Health Data Monitoring in Cardiovascular Diseases and Dementia

Christogianni, A. (2023) · Advances in information quality and management · doi:10.4018/978-1-6684-7366-5.ch014 · Cited by 4

Artificial Intelligence Data-Driven Petri nets Approach for Virtualizing Digital Twins

Júnior, A. O., ROLLE, J. L. C., & Leitão, P. (2023) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit58465.2023.10143087 · Cited by 3

CAD Modeling method of the electrospun membrane under multifractal dimension optimization control

Yang, M., Leng, X., & Ko, T. J. (2023) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0151528 · Cited by 3

Digital twin architecture for assembly line performance monitoring

Bonci, A., Biase, A. D., Giannini, M. C., Longhi, S., & Prist, M. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.01.107 · Cited by 4

Research on accurate morphology predictive control of CFETR multi-purpose overload robot

Zuo, C., Cheng, Y., Pan, H., Qin, G., Zhou, P., Xia, L., Wang, H., Zhao, R., Lv, Y., Qin, X., et al. (2024) · Nuclear Engineering and Technology · doi:10.1016/j.net.2024.06.003 · Cited by 3

Research on predictive control system for inspection robots based on digital twins

Liu, Y., Liu, H., Liu, M., & Liu, X. (2024) · International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC) · doi:10.1109/raiic61787.2024.10670982 · Cited by 3

A systematic quality-integrated diagnostic method for complex product assembly using multi-task spatial–temporal transfer learning

Cheng, X., Huang, F., & Qiu, L. (2024) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-024-14433-7 · Cited by 3

Automatic Quality Control System for Aircraft Assembly During the Whole Life Cycle Based on Digital Twin

Liu, R., Li, Y., Gao, C., Wu, H., Cheng, X., Xia, Y., Zhang, Y., & Liu, W. (2024) · IEEE International Conference on Sensors, Electronics and Computer Engineering (ICSECE) · doi:10.1109/icsece61636.2024.10729305 · Cited by 3

Quantum and AI-Enhanced Digital Twin Systems

Braga, D. M., & Rawal, B. S. (2024) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt61001.2024.10724642 · Cited by 3

PRISM: A Personalized, Rapid, and Immersive Skill Mastery Framework for Personalizing Experiential Learning Through Generative AI

Lin, Y.-Z., Patel, K., Alhamadah, A. H. J., Shih, B. P.-J., Redondo, M. W., Corona, D. R. V., Latibari, B. S., Pacheco, J., Salehi, S., & Satam, P. (2025) · Springer eBooks · doi:10.1007/978-3-032-01638-6_3 · Cited by 10

A Digital Twin‐based Intelligent Concrete Placing Technology

Zhu, Y., Zhong, G., Xu, J., & Huang, C. (2025) · ce/papers · doi:10.1002/cepa.3211 · Cited by 3

Leveraging Digital Twin Technology with a Human-Centered Approach to Automate a Workstation in the Logistics Sector of Made in Italy: CHIMAR Use Case

Bertoli, A., Nini, M., Cibrario, V., Vargas, M., Perona, P., Rossi, L., Benedetti, L., Nicolinti, A., & Fantuzzi, C. (2025) · Machines · doi:10.3390/machines13040303 · Cited by 3

Enhanced high-fidelity and dynamic modeling for power conditioning units synchronized with on-orbit satellites

Wang, M., Zhao, G., & Peng, X. (2025) · Aerospace Science and Technology · doi:10.1016/j.ast.2025.110213 · Cited by 3

Digital Twin Platform Based on Virtual Sensing for Real-Time Monitoring of Mechanical Features in Weaving Motion

Zhang, P., Zhang, J., Pan, R., Wang, L., Zhou, J., Zhang, N., & Xiang, J. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3559925 · Cited by 3

Human-Centered Digital Twin for Industry 5.0

Biondani, F., Capogrosso, L., Dall’Ora, N., Fraccaroli, E., Cristani, M., & Fummi, F. (2025) · Design, Automation, and Test in Europe · doi:10.23919/date64628.2025.10993218 · Cited by 4

Conceptual modelling method of multi-abstraction level digital twin based on extended RFLP model

Wu, C., & Cui, A. (2025) · Journal of Engineering Design · doi:10.1080/09544828.2025.2518905 · Cited by 3

Digital twin modeling method for wind turbine generator system based on operational mechanism and scenario fusion

Fei, X., Jiang, H., Zhou, J., & Zhou, Y. (2025) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-025-15892-2 · Cited by 3

Integrating Machine Learning into Asset Administration Shell: A Practical Example Using Industrial Control Valves

Marques, J. G., Medeiros, F. L., de Medeiros, P. L. F. F., Leitão, G. B. P., de Souza, D. C., Silva, D. R. C., & Guedes, L. A. (2025) · Processes · doi:10.3390/pr13072100 · Cited by 3

Digital twin driven opti-state control approach for smart warehousing in the synchronous operating environment

Zhang, Y., Qu, T., Hong, Z., Zhang, Z., & Huang, G. Q. (2025) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2025.103099 · Cited by 4

Proposed Student Digital Twin Data Model and System Architecture for Personalized Learning

Aboulsafa, E. I., Khayat, G. A. E., Abdul-Kader, H. M., & Almorsy, S. A. (2025) · International Conference on Machine Intelligence and Smart Innovation (ICMISI) · doi:10.1109/icmisi65108.2025.11115606 · Cited by 3

Reservoir computing for enhanced fidelity in hierarchical digital twin ecosystems

Mendula, M., Miozzo, M., Bellavista, P., & Dini, P. (2025) · Future Generation Computer Systems · doi:10.1016/j.future.2025.108146 · Cited by 3

Enhancing Industrial Internet of Things performance through deep transfer learning-based neural network digital twin modeling in data-scarce environments

Mahmood, T., Saba, T., Rehman, A. U., & Wang, Y. (2025) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2025.100956 · Cited by 3

Using Applied Computing on Embedded Computers to Build Digital Twins in a Fog Computing Environment

Zhukova, N. A., & Subbotin, A. (2023) · Mediterranean Conference on Embedded Computing (MECO) · doi:10.1109/meco58584.2023.10154931 · Cited by 3

Secure D2D Communications in 6G-oriented Networks for Multimedia eHealth Applications

Suraci, C., Marrara, G., Molinaro, A., & Araniti, G. (2023) · IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB) · doi:10.1109/bmsb58369.2023.10211168 · Cited by 3

User Acceptance of A Modular Interactive Digital Twin of The Production Department for Virtual Reality

Dvořák, M., Kubr, J., Bednář, M., Krákora, D., Malaga, M., Hořejší, P., & Simon, M. K. (2022) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci58124.2022.00214 · Cited by 3

Model Simplification: Addressing Digital Twin Challenges and Requirements in Manufacturing

de Ocaña, A. S., Bruch, J., & Aslanidou, I. (2023) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-43666-6_20 · Cited by 3

A Digital Twin Technology-Based Optimization Method for Energy Storage Evaluation of New Energy Power Systems

Liang, H., Su, J., & Deng, L. (2023) · International Conference on Energy Engineering and Power Systems (EEPS) · doi:10.1109/eeps58791.2023.10256990 · Cited by 3

Digital Twin in Oil and Gas Equipment for Predictive Maintenance

Dehghan, M. (2023) · Offshore Technology Conference Brasil · doi:10.4043/32857-ms · Cited by 3

Digital Twin is a complex of microservices

Pysin, M. D., & Лобанов, А. С. (2023) · E3S Web of Conferences · doi:10.1051/e3sconf/202345809012 · Cited by 3

Enhancement of damaged-image prediction based on digital twin technology

Guo, J. M., & Qi, D. (2023) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-023-00563-y · Cited by 3

Towards Generating Model-Driven Speech Interfaces for Digital Twins

Jayaraman, R., Lehner, D., Klikovits, S., & Wimmer, M. (2023) · ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C) · doi:10.1109/models-c59198.2023.00080 · Cited by 3

Low-cost “buffer” structural measure for flooding risk reduction in irrigation reservoirs

Espejo, F., Molina, J.-L., Zazo, S., Muñoz-Sánchez, R., & Patino-Alonso, M. C. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.131017 · Cited by 3

Human Factors in User Modeling for Intelligent Systems

Pera, M. S., Cena, F., Landoni, M., Musto, C., & Starke, A. D. (2024) · Human-computer interaction series · doi:10.1007/978-3-031-55109-3_1 · Cited by 3

Optimizing the Industrial Human-Machine Interface: Holographic Surfaces and Their Role in Anomaly Detection

Schloer, N., Boos, S., Harst, F., Lanquillon, C., Ohrnberger, M., Schoch, F., Stache, N. C., & Wittenberg, C. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-62110-9_47 · Cited by 3

Digital Twin-Based Model Reconfiguration Method for Ship Design Using NSGA-II

Zhang, J., Peng, L., Xu, W., Liu, J., & Hu, Y. (2023) · International Conference on Computing and Big Data (ICCBD) · doi:10.1109/iccbd59843.2023.10607301 · Cited by 3

Application of digital twin for industrial process control: A case study of gauge-looper-tension optimized control in strip hot rolling

Sun, J., Chen, S., Ding, C.-Y., Peng, W. J., & Zhang, D. (2024) · Digital Twin · doi:10.12688/digitaltwin.17971.1 · Cited by 5

Digital Twin of a battery-based EV charging station to support services

ROMERAL, P. A. D. A. F., Santos, G. R., Zancul, E. D. S., Gerz, J., Kehrer, M., Heimes, H. H., & Kampker, A. (2024) · Procedia CIRP · doi:10.1016/j.procir.2024.03.044 · Cited by 3

Edge Computing Empowered Digital Twin: An End-to-End Computing Task Scheduling Approach

Zhong, R., Feng, Y., Song, X., Hu, B., Wang, Y., Li, P., & Tan, J. (2024) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case59546.2024.10711331 · Cited by 3

Development of a Baseline Digital Twin Model as a Prerequisite for the Digital Twin Definition of a PSC-I Bridge with Model Updating Considering Member Stiffness

Roh, G., Park, J., Jeon, C.-H., & Shim, C.-S. (2024) · Buildings · doi:10.3390/buildings15010017 · Cited by 3

A Strain Field Reconstruction Method Based on Digital Twin by Multi-Level Fusion of Multi-Type Data

Wang, B., Ke, X., Wang, J., Liu, K., Song, Z., Zhou, J. P., & Zhou, C. (2025) · Experimental Mechanics · doi:10.1007/s11340-025-01183-0 · Cited by 3

Towards a Digital Twin Power System: A Case Study for Wind Power Forecasting

Chen, C., Yang, G., Hu, W., & Dai, X. (2024) · International Conference on Smart Power & Internet Energy Systems (SPIES) · doi:10.1109/spies63782.2024.10983232 · Cited by 3

Using AI enhanced agent-based models to support management of wild populations

Willoughby, J. R., Lamka, G. F., Dunning, K. H., Narine, L. L., Belsare, A., & Sundaram, M. (2025) · Landscape Ecology · doi:10.1007/s10980-025-02149-2 · Cited by 3

A Graph-Based Model Reduction Method for Digital Twins

Chakraborti, A., Vainio, H., Koskinen, K. T., & Lammi, J. (2023) · Machines · doi:10.3390/machines11070733 · Cited by 4

A Hybrid Machine Learning and Optimization Algorithm for Enhanced User Comfort and Energy Efficiency in Smart HomesPreprint

Khan, J. A., Fayaz, M., Zaman, U., Lee, E., Balobaid, A. S., Aburasain, R. Y., Ali, S., & Kim, K. (2024) · Preprints.org · doi:10.20944/preprints202401.1331.v1 · Cited by 3

Application of Combination of Digital Twin and Machine Learning in Production System OptimizationPreprint

Zhang, D., Ni, Z., Quan, X., & Du, A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5179510 · Cited by 3

Digital Twin Model of Treatment Outcomes in Post-Stroke AphasiaPreprint

Busby, N., Riccardi, N., Wilmskoetter, J., Jeakle, E. N., Newman-Norlund, R., Kristinsson, S., Rorden, C., Fridriksson, J., & Bonilha, L. (2026) · medRxiv · doi:10.64898/2026.02.03.26345022 · Cited by 3

Modelling of blast furnace ironmaking process based on digital twin

Xu, Y., Yang, C., & Liu, Z. (2024) · Digital twins and applications. · doi:10.1049/dgt2.12016 · Cited by 2

A Model Development Approach Based on Point Cloud Reconstruction and Mapping Texture Enhancement

You, B., & Asli, B. H. S. (2024) · Big Data and Cognitive Computing · doi:10.3390/bdcc8110164 · Cited by 2

IoT Integration and Digital Twin Technology in Smart Manufacturing: Advancements and Prospects

Onu, P., Pradhan, A., & Madonsela, N. S. (2024) · International Multidisciplinary Information Technology and Engineering Conference (IMITEC) · doi:10.1109/imitec60221.2024.10851037 · Cited by 5

Offline and Online Energy Simulation using Virtual Commissioning Models with Extended Dynamical Behavior

Deubert, D., Pfeifer, D., Scheifele, C., Fehr, J., & Verl, A. W. (2025) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/sii59315.2025.10870981 · Cited by 2

Data-driven Modeling for Grid Edge IBRs: A Digital Twin Perspective of User-Defined Models

Mahapatra, K., Mitra, B., & Kundu, S. (2024) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon55916.2024.10905168 · Cited by 2

Developing a digital twin system for a direct-drive electric screw press

Liu, L., Hu, X., Quan, Z., Huang, J., & Liu, J. (2025) · CIRP journal of manufacturing science and technology · doi:10.1016/j.cirpj.2025.04.015 · Cited by 2

An integrated simulator towards a digital twin for MW-class Gyrotrons for fusion reactors

Novarese, E., Cammi, A., Difonzo, R., Introini, C., & Savoldi, L. (2025) · Fusion Engineering and Design · doi:10.1016/j.fusengdes.2025.115157 · Cited by 2

A dynamic assembly and fusion approach for digital twin logic models based on petri nets

Liu, P., Miao, Y., Jiao, L., Liu, T., & Ni, Z. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.105527 · Cited by 2

Research on Data Prediction Model for Aerodynamic Drag Reduction Effect in Platooning Vehicles

Wang, Z., Guo, X., Yang, N., Liu, S., Chen, L., Zhang, Z., & Zhu, C. (2025) · Processes · doi:10.3390/pr13072056 · Cited by 3

A Meta-Framework for Virtual Entities Within Digital Twins: Better Understanding for Stakeholders and Technologies

Klaassen, C., Steindl, G., Kasper, L., & Hofmann, R. (2025) · IEEE Access · doi:10.1109/access.2025.3589787 · Cited by 2

Spatial-temporal traceability and coordination in modular integrated construction: a future framework with hybrid digital twin and reinforcement learning

Li, Y., & Zhong, R. Y. (2025) · International Journal of Construction Management · doi:10.1080/15623599.2025.2537727 · Cited by 3

PointDreamer: Zero-Shot 3D Textured Mesh Reconstruction From Colored Point Cloud

Qiao, Y., Li, X., Tang, Y., Han, X., Xu, J., Hu, L., & Chen, M. (2025) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2025.3595987 · Cited by 2

The Two Faces of Interoperability: Bridging Cyber and Physical Spaces with Digital Twins

Picone, M., Martinelli, M., Burattini, S., Giulianelli, A., & Ricci, A. (2025) · International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) · doi:10.1109/dcoss-iot65416.2025.00078 · Cited by 2

Optimising blank time in discrete-time sampling: time-scheduling method for near real-time simulation

Xia, T., Zuo, J., Wang, T., Xu, Z., & Ding, J. (2025) · Journal of Simulation · doi:10.1080/17477778.2025.2536083 · Cited by 2

ScanVerse: An extended reality authoring platform for industrial digital twins

Solmaz, S., Jorissen, L., & Zogopoulos, V. (2025) · Procedia CIRP · doi:10.1016/j.procir.2025.08.107 · Cited by 2

Toward efficient digital twin simulation: A causal representation learning approach

Luo, S., Qian, J., Geng, Y., Zhou, Q., & Lin, Q. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114442 · Cited by 2

Real-Time Blockage Detection and Autonomous Recovery in Liquid-Cooled Systems Using Digital Twins

Worch, J., Sado, K. A., Downey, A. R. J., Khan, J. A., & Santi, E. (2025) · IEEE Electric Ship Technologies Symposium (ESTS) · doi:10.1109/ests62818.2025.11152430 · Cited by 2

Research on the construction and fidelity evaluation of digital twins for CNC machine tools

Zhang, X., Chen, W., Li, J., Zhao, Z., Yin, Z., Mu, Z., & Wang, H. (2025) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-025-16580-x · Cited by 2

Driving Industry 4.0 with Digital Twins: Enhancing Predictive Maintenance and Operational Performance Through IoT and Machine Learning

Ahmed, K. R., Sultana, N., Nesar, S. T., Hosien, M. A., Karim, M. R., Khan, M. S., & Chowdhury, M. A. R. (2025) · IEEE International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN) · doi:10.1109/qpain66474.2025.11172010 · Cited by 2

A feature extraction method for multi-source uncertainty adaptive fusion in complex equipment failure mode recognition

Wang, D., Sun, J., Liu, Z., Sa, G., & Tan, J. (2025) · Computers & Industrial Engineering · doi:10.1016/j.cie.2025.111576 · Cited by 2

A Comparative Study of UniTwin and Established Digital Twin Frameworks for IoT Applications

Häußermann, T. M., Lehmann, J., Rache, A., Kolb, F., & Reichwald, J. (2025) · IEEE Access · doi:10.1109/access.2025.3619153 · Cited by 2

Recovering the Reduced Scattering and Absorption Coefficients of Turbid Media from a Single Image

Nguyen, P. H., Hevisov, D., Foschum, F., & Kienle, A. (2025) · Photonics · doi:10.3390/photonics12111118 · Cited by 2

Multiscale geometric digital twins of mine tunnels that integrate topological evolution and local reconstruction

Cui, Y., Li, J., Li, Y., Zhang, Z., & Du, J. (2025) · Tunnelling and Underground Space Technology · doi:10.1016/j.tust.2025.107317 · Cited by 3

Automatic Generation of Digital Twins for Network Testing

Ding, S., Flynn, D., & Harvey, P. (2025) · IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW) · doi:10.1109/icdcsw63273.2025.00122 · Cited by 2

Motor fault digital twin modelling and data generation

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