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

Application of Digital Twin Model Based on Parameter Encoding in the Calibration of UHVDC Transmission System

Yu, J., Yin, X., Zhou, F., Liang, S., Yue, C., Sun, F., & Tan, D. P. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3584455 · Cited by 1

AI-Driven Intelligent Decision Support System for Digital Twin Industrial Operation

Li, M. (2025) · Journal of Circuits Systems and Computers · doi:10.1142/s0218126625504419 · Cited by 1

Fault Diagnosis for Grid-Connected Photovoltaic Systems Based on Digital Twins

Qiao, J., Qi, Y., Liu, L., Li, Y., & Shi, S. (2025) · IEEE International Symposium on the Application of Artificial Intelligence in Electrical Engineering (AAIEE) · doi:10.1109/aaiee64965.2025.11100437 · Cited by 1

Photovoltaic output power prediction based on thermoelectric coupling digital twin model of inverter

Mou, H., Chen, Y. Y., Jiang, X., Liu, T., & Zhang, J. (2025) · Journal of Computational Methods in Sciences and Engineering · doi:10.1177/14727978251366546 · Cited by 1

A review of digital twin-enabled intelligent manufacturing with a focus on the wooden furniture industry

Hu, C., & Zhu, J. (2025) · Wood Material Science and Engineering · doi:10.1080/17480272.2025.2551742 · Cited by 1

A review: digital twin application in bridge fire engineering and management

Mohammed, O. M., & Zhu, Y. (2025) · Digital Twin · doi:10.1080/27525783.2025.2545236 · Cited by 1

Research on data-driven and model-driven methods for compensating machine tool feed system transient errors

Lu, H., Yang, K., Zhang, Y., Wang, R., Liu, Q., Li, Z., He, J., Xiao, J., & Shi, X. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/ae01c9 · Cited by 1

Automated Integration of Surgical Implants Into Digital Twins for Trauma Surgery

Stauffer, T., Reber, M., Fellmann, L., Babst, R. H., Meboldt, M., & Lohmeyer, Q. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05114-1_6 · Cited by 1

Multiphysics coupling AI prediction method for thermomechanical behavior of steel ladle linings

Yin, Y., Long, Z., Jin, S., Li, Y., Wang, F., & Xu, X. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114495 · Cited by 1

Clock synchronization method for networks of digital-physical fusion testing under complex dynamic testing circumstances

Wang, Y., Zou, X., Guan, L., & Tao, F. (2025) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-025-16564-x · Cited by 1

The Concept of a Digital Twin of an Autonomous Mobile Robot

Natmeladze, N., Piteľ, J., Židek, K., Romaniuk, V., & Bonello, A. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-03722-0_15 · Cited by 3

Research on the application of a digital twin system for regulating devices oriented to multi-physics field fusion

Xie, L., Zhang, L., Zou, Z., Yang, Z., & Che, G. (2026) · Flow Measurement and Instrumentation · doi:10.1016/j.flowmeasinst.2026.103372 · Cited by 1

Forest Fire Analysis Prediction and Digital Twin Verification: A Trinity Framework and Application

Li, W., Zai, W., Fan, W., & Tang, Y. (2025) · Forests · doi:10.3390/f16101546 · Cited by 1

Towards Automating the Life Cycle Management of Digital Twins

Beaumont, G. G., Beugnard, A., Martínez, S., Urtado, C., & Vauttier, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-08623-5_22 · Cited by 1

Modeling corporate management depending on the range of activity and business processes digitalization level

Валериевна, А. Г., & MAZANKOVA, T. V. (2025) · Finance and Credit · doi:10.24891/yelpkt · Cited by 1

Commissioning-based framework to enhance asset life cycles in manufacturing environments

Pronk, L., Liefhebber, R., Damgrave, R., & Lutters, E. (2025) · Production & Manufacturing Research · doi:10.1080/21693277.2025.2590672 · Cited by 1

A data -driven Automatic Model Generation Methodology of Digital Twin models for cost-effective adaptability and scalability in manufacturing systems

Villegas, L. F., Palmitessa, E., Macchi, M., & Polenghi, A. (2025) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2025.11.847 · Cited by 1

Fault data augmentation for electromechanical coupling systems based on hierarchical collaborative digital twin

Xuanyuan, S. U., Tao, L., Sun, B. N., Jin, K., Yongzhe, M. A., Wang, X., Lü, C., & Ding, Y. (2025) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2025.104005 · Cited by 1

Flow-Based Bayesian Updating for Finite Element Models in Digital Twins

Wei, S., Qian, C., Li, W., Ren, Y., & Fan, D. (2025) · IEEE Annual Reliability and Maintainability Symposium - Europe (RAMS-Europe) · doi:10.1109/rams-europe62094.2025.11274862 · Cited by 1

Digital Twins as Predictive Models for Real-Time Probabilistic Risk Assessment of Autonomous Vehicles

Vadnerkar, K. M., & Pisu, P. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3641530 · Cited by 1

Simulation as a Service in Data Spaces: A Digital Twin-based Approach

Coppolino, L., Parreño-Rodríguez, A., Petruolo, A., Sánchez-Valverde, J., & Skarmeta-Gómez, A. F. (2025) · Data Science and Engineering · doi:10.1007/s41019-025-00305-x · Cited by 1

Digital twin-based energy efficiency optimization of robotic pick-and-place process under uncertain payload

Peng, L., Yao, W., Qi, L., Xu, W., & Liu, J. (2025) · Computers & Industrial Engineering · doi:10.1016/j.cie.2025.111608 · Cited by 1

Enhancing computational efficiency in digital twins: a survey of techniques and challenges for fast inference

Dantas, M., Silva, M. L., Filho, A. T. D. O., Bezerra, D. D. F., Freitas, E., do Carmo, P. R. X., Sadok, D. F. H., Kelner, J., & Souza, R. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06916-1 · Cited by 1

A Digital Twin of Life-Cycle Bearing Based on a Defect-Guided Conditional Diffusion Model for Predicting Remaining Life under Limited Degradation Data

Deng, J., Wen, G., Lei, Z., Xu, Q., Su, Y., & Zhang, Z. (2025) · ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A Civil Engineering · doi:10.1061/ajrua6.rueng-1786 · Cited by 1

Theoretical model of an energy-harvesting temperature sensor device using magnetic moment change

Kiyomoto, H., Sakai, Y., & Kansha, Y. (2026) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2026.01.003 · Cited by 1

RESEARCH PROGRESS AND CHALLENGES OF DIGITAL TWIN TECHNOLOGY IN THE FIELD OF AGRICULTURAL MACHINERY TOOL WEAR

ZHANG, Y.-J., Hua, Z. H. A. N., Jia-peng, W. U., & Rui-Jun, W. A. N. G. (2025) · INMATEH Agricultural Engineering · doi:10.35633/inmateh-77-88 · Cited by 1

Toward virtual bladder: real-time bladder volume monitoring with flexible AuCNT strain sensors

Cho, Y., Jo, Y., Kang, M., Shin, H., Cho, J. J., Jeong, H., Suh, H.-S., Pak, C. J., Park, J., Kwon, S., et al. (2026) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2025.1717576 · Cited by 1

Efficient Torque Prediction for Digital Twins in Quarry Operations: A Data-Driven and Expert-Guided Approach

Habbab, A., Fattouh, A., Loni, M., Chirumalla, K., Frank, B., & Bohlin, M. (2025) · IEEE International Conference on Industrial Informatics (INDIN) · doi:10.1109/indin64977.2025.11279455 · Cited by 1

A mechanism and data-driven adaptive update framework for digital twin model of electric drive assembly gear transmission system

Pengbo, Z., Chen, R., Dong, L., Mengyu, R., Hepeng, L., Yi, A., & Liang, G. (2026) · Journal of Vibration and Control · doi:10.1177/10775463251413556 · Cited by 1

Dynamic simulation and optimization approach for coevolutionary intelligent manufacturing cell with digital twin

Leng, S., Yang, X., Zenghua, J., Wei, Y., & Wang, C. (2026) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-025-17221-z · Cited by 1

Review on digital twin applications in medical particle accelerators

Kambondo, A., & Wang, J. (2026) · Nuclear Engineering and Technology · doi:10.1016/j.net.2026.104153 · Cited by 1

A multi-level multi-source digital twin model for performance enhancement and optimization decision-making in precision milling machines

Xie, Y., Mei, S., Wang, F., & Zhang, C. (2026) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2026.101080 · Cited by 1

Shop floor environment digital twin scan for extended reality applications

Solmaz, S., Mahy, W., & Zogopoulos, V. (2026) · Procedia CIRP · doi:10.1016/j.procir.2026.01.194 · Cited by 1

The network digital twin in Internet of Vehicles: applications, modeling, and challenges

Ghannay, S., & Gammar, S. M. (2026) · Annals of Telecommunications · doi:10.1007/s12243-026-01164-x · Cited by 1

Physics informed virtual sensor modeling for wind power prediction

Liu, A., Zhang, K., Jia, M., Liu, Z., Yang, Q., & Liu, Y. (2026) · Energy · doi:10.1016/j.energy.2026.140691 · Cited by 1

A sample-free wind turbine gearbox fault diagnosis approach integrating amplitude sensitivity permutation entropy with MSCA-CNN

Xiao, K., Zhou, J., Cao, Z., Fei, X., & Zhou, Y. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae4f12 · Cited by 1

Degradation-adaptive digital twin for long-term consistency

Xia, T., Zuo, J., Ding, J., & Pan, Y. (2026) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2026.103276 · Cited by 1

Systems of Digital Twins: A Systematic Literature Review and a formal framework

Gervasi, M., Bove, L., Corallo, A., Miglietta, M., Negro, L., Pellegrino, G., Ria, F., & Visaggio, C. A. (2026) · Digital engineering. · doi:10.1016/j.dte.2026.100100 · Cited by 1

Digital twin for power equipment: lifecycle framework, enabling technologies, and prospects in next-generation power systems

Wang, D., Liu, Q., Yang, J., Xu, X., Chen, X., Shi, Y., Huang, D., Wang, Y., & Wang, K. (2026) · Computing · doi:10.1007/s00607-026-01650-z · Cited by 3

Unveiling code clones in the Eclipse IIoT software ecosystem

Li, Z., Huang, B., Li, Y., Mo, R., Liang, P., Liu, H., & Ma, Y. (2026) · Journal of Systems and Software · doi:10.1016/j.jss.2026.112869 · Cited by 1

Accelerating biopharmaceutical innovation: A review of machine learning across the manufacturing pipeline

Guo, J., Xi, L., & Gao, H. (2026) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2026.04.014 · Cited by 1

A management framework for systematic data integration empowered for digital twin in collaborative manufacturing

Liu, H., Qu, T., Li, S., Ma, L., Zhang, K., & Huang, G. Q. (2026) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2026.04.017 · Cited by 1

Integrated assessment of energy systems: opportunities and barriers for digital twin and artificial intelligence adoption

Amponsah, J., Ansah, K. K., Asare, D. B., & Siameh, F. O. (2026) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2026.116962 · Cited by 2

RadioMapMotion: A Dataset and Benchmark for Proactive Spatio-Temporal Radio Environment Prediction

Cheng, N., Jia, H., Wang, X., Peng, H., Sun, R., & Zhou, C. (2026) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2026.3685413 · Cited by 1

Evaluating Software Platforms for Digital Twin Creation: Features, Capabilities, and Tradeoffs

Flores-Martin, D., Rodriguez-Cabanillas, A., & Berrocal, J. (2026) · IT Professional · doi:10.1109/mitp.2026.3661302 · Cited by 1

REDTAIL: Leveraging Remote Experimentation and Digital Twins to Promote Engineering Learning Experiences

Zhang, Z., Hussein, R., & Orduña, P. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-07319-8_29 · Cited by 1

Spatial modelling for bridge construction environment towards geospatial digital twin

Li, W., Zhu, J., Zhang, J., Wu, J., Zhu, Q., & Dehbi, Y. (2026) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2026.105296 · Cited by 1

Immune-derived biological age as a machine learning predictor of health in the honey bee model

Frunze, O., Kim, H., Yun, Y., Woo, S., Lee, S.-J., Bae, Y., & Kwon, H. W. (2026) · Ecological Informatics · doi:10.1016/j.ecoinf.2026.103797 · Cited by 1

Cross-condition bearing fault diagnosis under data imbalance using adversarial domain adaptation signal alignment digital twin

Zhou, Z., Liu, C., & Chen, M. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae6a03 · Cited by 1

Digital Twins as a Bridge Between Industry 5.0 and Education 5.0

Sterev, N., Miltchev, R., & Yordanov, D. (2026) · IGI Global eBooks · doi:10.4018/979-8-2600-1669-5.ch009 · Cited by 1

Utilizing Manufacturing Digital Twins for Sustainability Reporting

Luo, Y., Candia, J. R., & Ball, P. D. (2026) · Lecture notes in mechanical engineering · doi:10.1007/978-3-032-21154-5_32 · Cited by 1

Real-Time Structural Stress Monitoring of Gantry Cranes Utilizing Digital Twin Technology

Liu, M., Chen, K. T., Xiao, H., Zhu, Z., Yan, Y., Zhang, J., Han, Y., & Zhu, F. (2026) · Applied Sciences · doi:10.3390/app16104870 · Cited by 1

Digital Twin-Enabled Robust Parallel Control of Construction Engineering Equipment Under Uncertainty

Chen, R., Xu, H., Zhang, L., Rong, J., Wei, C., Chang, H., & Zhang, H. (2026) · Buildings · doi:10.3390/buildings16101982 · Cited by 1

Digital Twin Architecture for Drone Logistics Operations

Pachayappan, M., Kundu, T., Wagner, S. M., & Sundarakani, B. (2026) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2026.3695002 · Cited by 2

An Intelligent IoT-Based Predictive Control System for Water Quality and Energy Management in Koi Aquaculture

Kularbphettong, K., Kaewrattanapat, N., & Raksuntorn, N. (2026) · Sensors · doi:10.3390/s26103238 · Cited by 1

A deep learning approach based small object detection for dynamic digital twins in smart manufacturing

Gupta, V., & Mangal, D. (2026) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2026.111246 · Cited by 1

Intelligent task offloading in vehicular edge computing: a hybrid framework combining informer-based prediction and deep reinforcement learning

Yao, Z., Zhu, Q., & Qin, P. (2026) · Cluster Computing · doi:10.1007/s10586-026-06111-y · Cited by 1

Dynamic Thermal Network Parameter Updating Strategy for IGBT Full-Bridge Modules in Digital Twin Applications

Shen, J., Zhang, L., WANG, C., Sun, S., & Zhao, D. (2026) · Energies · doi:10.3390/en19132999 · Cited by 1

Data quality over model complexity: A leakage-aware, horizon-aware forecasting pipeline for degraded sensor streams in a small-population wastewater treatment plant

Bravo, J. M., Rodríguez, C. M. P., Berrocal, J., Herrera, J. L., & Pérez, J. P. C. (2026) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1016/j.cacaie.2026.100134 · Cited by 1

Digital twins for soft pneumatic actuators via reduced-order surrogate modeling

Pu, Y., Hu, X., Liu, X., Niu, S., & An, N. (2026) · International Journal of Solids and Structures · doi:10.1016/j.ijsolstr.2026.114190 · Cited by 1

A digital twin-based study on the measurement and control of coupled rock-equipment response behaviors in steeply dipping stopes

Zhang, Y., Xie, P., Wu, Y., & Huang, B. (2026) · Measurement · doi:10.1016/j.measurement.2026.122600 · Cited by 1

Towards adaptive closed-loop optimization: A review of sensor placement for digital twins in mechanical systems

Li, Y., He, X., Wang, M., Liu, C., Pang, Y., & Song, X. (2026) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2026.07.027 · Cited by 1

Digital Twin shop-floor: A Complex System-oriented Construction Method and Operation Mechanism

Liu, H., Zhang, J., Cheng, Y., Tao, F., & Qi, Q. (2023) · Procedia CIRP · doi:10.1016/j.procir.2023.03.083 · Cited by 1

Bridging the Gap From Concrete Building Construction to Flexible Software Process—Way of Working

Sophatsathit, N. (2023) · IT Professional · doi:10.1109/mitp.2023.3288679 · Cited by 1

Towards the Development of a Digital Twin for Micro Learning Factory: A Proof of Concept

Sow, M. C., Assila, A., García, D., Martínez-Martínez, S., Zghal, M., & Baudry, D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43401-3_19 · Cited by 1

Automatically Quantifying Movement of Prefabricated Building Components on Site for a Location-Based Management System: An Ecosystem for Digital Twin Construction

Corrêa, F. R., Maciel, A. R., & Scheer, S. (2023) · Lecture notes in civil engineering · doi:10.1007/978-3-031-35399-4_44 · Cited by 1

A Model-Driven Platform for Engineering Holistic Digital Twins

Lehner, D. (2023) · ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C) · doi:10.1109/models-c59198.2023.00045 · Cited by 1

Digital Twin: Conclusion and Future Trends in Process Plants

Stjepandić, J., Lützenberger, J., & Kremer, P. (2024) · Springer eBooks · doi:10.1007/978-3-031-47316-6_10 · Cited by 1

Digital Twin Framework for Human to Environment Interaction in Interactive Spaces

Prasetyo, D., Fachri, M., Ramadhani, N., Damastuti, F. A., Hariadi, M., & Mutiaz, I. R. (2023) · International Conference on Informatics and Computing (ICIC) · doi:10.1109/icic60109.2023.10382118 · Cited by 1

An enhanced state-aware model learning approach for security analysis in lightweight protocol implementations

Guo, J., Zhao, D., Gu, C., Chen, X., Zhang, X., & Ju, M. (2024) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-024-00593-0 · Cited by 1

Digital Twin Enabled Fault Detection Method for RRAM-Based Neural Computing Systems

Han, Q., & Yuan, D. (2023) · IEEE International Conference on Memristive Computing and Applications (ICMCA) · doi:10.1109/icmca59770.2023.10481195 · Cited by 1

Design and Implementation of a Digital Twin System for Monitoring Automated Container Terminal Equipment

Lu, H., Zhang, B., & Hou, L. (2024) · Mechatronics and intelligent transportation systems · doi:10.56578/mits030105 · Cited by 1

Digital Twin-Based Energy-Efficient Trajectory Optimization for Robotic Pick-and-Place Process Under Uncertain Payload

Yao, W., Xu, W., Liu, J., Yang, H., & Zhou, Z. (2024) · Lecture notes in mechanical engineering · doi:10.1007/978-3-031-52649-7_12 · Cited by 1

Virtual Model of a Robotic Arm Digital Twin with MuJoCo

Inturias, B. P., de Oliveira, J. P. G. M., & Becerra-Vargas, M. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-59057-3_29 · Cited by 1

A Digital Twin Framework of In-Line Process Optimisation for Material Extrusion-Based Additive Manufacturing

Yin, K., Lou, S., Qin, Y., Xu, Y., Scott, P. J., & Jiang, X. (2024) · Mechanisms and machine science · doi:10.1007/978-3-031-49421-5_63 · Cited by 1

Research on Physical Reliability Rendering Algorithm

Wang, X., & Meng, L. (2024) · IEEE Access · doi:10.1109/access.2024.3430535 · Cited by 1

DECEPTWIN: Proactive Security Approach for IoV by Leveraging Deception-based Digital Twins and Blockchain

Iqbal, M., Suhail, S., & Matulevičius, R. (2024) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1145/3664476.3670473 · Cited by 1

Digital Twinning for Resilient Supply Chain under Cash-Flow Constraint

Mishra, S., & Venkateswaran, J. (2024) · IEEE International Conference on Software Architecture Companion (ICSA-C) · doi:10.1109/icsa-c63560.2024.00062 · Cited by 1

Afet Dirençliliğinde Dijital Dönüşüm: Sismik Dijital İkizlerin Rolü

Korkmaz, M., Zülfikar, A. C., & Demirkesen, S. (2024) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub455.c1943 · Cited by 1

Lightweight Web3D Twinning Fire Evacuation Simulation of Metro Station

Li, J., Chen, N., & Jia, J. (2024) · international conference on 3D web technology · doi:10.1145/3665318.3677153 · Cited by 1

Development of a digital twin system for acquiring surface features of solid models in light-curing additive manufacturing

Zheng, Z.-J., Wang, Y., Li, J., & An, Z. (2024) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-024-14535-2 · Cited by 1

Application of Virtual Production to Optimize Industrial Production Units

Emese, P., Kis, K. Á., & Sarvajcz, K. (2024) · Advances in Science and Engineering Technology International Conferences (ASET) · doi:10.1109/aset60340.2024.10708739 · Cited by 1

Model Updating for Structural Digital Twins Through Physics-Informed Data-Driven Models

Radbakhsh, S. H., Nik‐Bakht, M., & Zandi, K. (2024) · Lecture notes in civil engineering · doi:10.1007/978-3-031-62170-3_9 · Cited by 1

Bearing Fault Diagnosis Method Based on Mechanical-Data Fusion Drive

Zhang, P., Li, M., Xiao, Y., & Zhang, X. (2024) · International Conference on Computer Science and Blockchain (CCSB) · doi:10.1109/ccsb63463.2024.10735669 · Cited by 1

Tribo-Dynamics Digital Twins (TDDTs): Prediction of Friction and Frequency Response Function (FRF) in a Dry Sliding Tribological Contact

Taghizadeh, S., Bonney, M. S., Wagg, D., & Ghadbeigi, H. (2024) · Conference proceedings of the Society for Experimental Mechanics · doi:10.1007/978-3-031-68893-5_15 · Cited by 1

Digital twin technology for energy flexibility and saving

Avendano, I. A. C., Javan, F. D., Najafi, B., Aghaei, M., Moazami, A., & Rinaldi, F. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-14070-9.00011-1 · Cited by 1

A Digital Twin Approach to Building’s Dossier for Seismic Prevention

Gasperis, G. D., & Facchini, S. D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70415-4_8 · Cited by 1

Digital Twin Technology as Facilitator to Sustainable and Energy-Efficient Ecosystem

Sharma, S., Singh, J., & Rahul, J. (2024) · Advances in intelligent systems and computing · doi:10.1007/978-981-97-6349-8_5 · Cited by 1

Demagnetization Prediction Method of Permanent Magnet Synchronous Motor Based on Digital Twin Model

Xu, S., Zhang, Z., Huang, Y., & Han, T. (2024) · China Automation Congress (CAC) · doi:10.1109/cac63892.2024.10865195 · Cited by 1

Construction Method of a Virtual Debugging System for Equipment Based on Digital Twin

Shen, Y., Meng, Q., Dong, X., Li, X., Xu, M., Zhang, Q., & Yang, H. (2024) · China Automation Congress (CAC) · doi:10.1109/cac63892.2024.10864874 · Cited by 1

Application of Modeling and Simulation in a Self-Reprogrammable Prototype of a Manufacturing System

Ferro, R., Oliveira, J. D. D., Cordeiro, G., & Ordonez, R. (2025) · Applied Sciences · doi:10.3390/app15063298 · Cited by 1

Research on Parameter Influence of Offshore Wind Turbines Based on Measured Data Analysis

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