Make more digital twins
Open accessAbstract
Virtual models boost smart manufacturing by simulating decisions and optimization, from design to operations, explain Fei Tao and Qinglin Qi. Virtual models boost smart manufacturing by simulating decisions and optimization, from design to operations, explain Fei Tao and Qinglin Qi.
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Fragmentation of data ownership, disparate data types, and challenges related to patenting and proprietary knowledge limit accessibility and integration.
“The fragmentation of data ownership, disparate data types, and challenges related to patenting and proprietary knowledge further limit accessibility and integration [133].”
From Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0 (2025) · cites this paper as [133]
In “Make more digital twins” (page unavailable), the paper identifies scattered data ownership, incompatible formats and difficulty merging disparate data types as barriers. It also says companies are reluctant to share commercially sensitive information, but it does not address patenting specifically. (LLM, conf=0.86)
Experimental — accuracy not yet measuredSynthetic datasets are artificially generated to reflect the characteristics of real patient data and can be used to train and test models without exposing sensitive information.
“Synthetic datasets are artificially generated to reflect the characteristics of real patient data and can be used to train and test models without exposing sensitive information [80].”
From Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22. (2025) · cites this paper as [80]
The opening section describes digital twins as virtual copies of machines or systems, and 'Unify data and model standards' discusses sharing models and healthcare data standards. Neither addresses synthetic patient datasets, their use to train or test models, or protection of sensitive information (searched: synthetic patient data, training models, privacy). (LLM, conf=0.76)
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Cites
Smart manufacturing must embrace big data
Kusiak, A. (2017) · Nature · doi:10.1038/544023a · Cited by 636 · see citing works
Digital Twin Driven Smart Manufacturing
Tao, F., Zhang, M., & Nee, A. Y. C. (2019) · Elsevier eBooks · doi:10.1016/c2018-0-02206-9 · Cited by 284 · see citing works
Flagged first, then by citations
Research works printed but not linked: 10
- Biagioli, M. Galileo, Courtier: The Practice of Science in the Culture of Absolutism Ch. 2 (Univ. Chicago Press, 1992).
- Morus, I. R. When Physics Became King (Univ. Chicago Press, 2005).
- Clark, W. Academic Charisma and the Origins of the Research University (Univ. Chicago Press, 2006).
- Cahan, D. An Institute for an Empire: The Physikalisch-Technische Reichsanstalt, 1871-1918 (Cambridge Univ. Press, 1989).
- Coen, D. R. Climate in Motion: Science, Empire, and the Problem of Scale (Univ. Chicago Press, 2018).
- Kikuchi, Y. Anglo-American Connections in Japanese Chemistry: The Lab as Contact Zone (Palgrave Macmillan, 2013).
- Krige, J. American Hegemony and the Postwar Reconstruction of Science in Europe (MIT Press, 2006).
- Kaiser, D. Drawing Theories Apart: The Dispersion of Feynman Diagrams in Postwar Physics Ch. 4 (Univ. Chicago Press, 2005).
- Moore, K. Disrupting Science: Social Movements, American Scientists, and the Politics of the Military, 1945-1975 (Princeton Univ. Press, 2008).
- Mirowski, P. Science-Mart: Privatizing American Science (Harvard Univ. Press, 2011).
Cited by
Sorted by citations
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 · see citing works
Self-citationTao, F., Xiao, B., Qi, Q., Cheng, J., & Ji, P. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2022.06.015 · Cited by 1,255 · see citing works
Digital Twins: State of the art theory and practice, challenges, and open research questions
Sharma, A., Kosasih, E., Zhang, J., Brintrup, A., & Calinescu, A. (2022) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2022.100383 · Cited by 587 · see citing works
Digital twins to personalize medicine
on behalf of the Swedish Digital Twin Consortium, Björnsson, B., Borrebaeck, C., Elander, N., Gasslander, T., Gawel, D. R., Gustafsson, M., Jörnsten, R., Lee, E. J., Li, X., et al. (2020) · Genome Medicine · doi:10.1186/s13073-019-0701-3 · Cited by 566 · see citing works
Fusing Blockchain and AI With Metaverse: A Survey
Yang, Q., Zhao, Y., Huang, H., Xiong, Z., Kang, J., & Zheng, Z. (2022) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2022.3188249 · Cited by 576 · see citing works
A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin
Luo, W., Hu, T., Ye, Y., Zhang, C., & Wei, Y. (2020) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2020.101974 · Cited by 525 · see citing works
A digital twin of Earth for the green transition
Bauer, P., Stevens, B., & Hazeleger, W. (2021) · Nature Climate Change · doi:10.1038/s41558-021-00986-y · Cited by 442 · see citing works
Digital twin in healthcare: Recent updates and challenges
Sun, T., He, X., & Li, Z. (2023) · Digital Health · doi:10.1177/20552076221149651 · Cited by 421 · see citing works
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 · see citing works
Digital Twin Enhanced Dynamic Job-Shop Scheduling
Zhang, M., Tao, F., & Nee, A. Y. C. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.04.008 · Cited by 348 · see citing works
Digital twin for healthcare systems
Vallée, A. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2023.1253050 · Cited by 359 · see citing works
Smart city based on digital twins
Li, D.-R., Yu, W.-B., & Shao, Z.-F. (2021) · Computational Urban Science · doi:10.1007/s43762-021-00005-y · Cited by 264 · see citing works
Digital twin modeling method based on biomimicry for machining aerospace components
Liu, S., Bao, J., Lu, Y., Li, J., Lu, S., & Sun, X. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.04.014 · Cited by 310 · see citing works
Recent advances on industrial data-driven energy savings: Digital twins and infrastructures
Teng, S. Y., Touš, M., Leong, W. D., How, B. S., Lam, H. L., & Máša, V. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110208 · Cited by 315 · see citing works
Ramu, S. P., Boopalan, P., Pham, Q.-V., Maddikunta, P. K. R., Huynh-The, T., Alazab, M., Nguyen, T. T., & Gadekallu, T. R. (2022) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103663 · Cited by 301 · see citing works
A Survey on Digital Twin for Industrial Internet of Things: Applications, Technologies and Tools
Xu, H., Wu, J., Pan, Q., Guan, X., & Guizani, M. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3297395 · Cited by 304 · see citing works
Zhang, G., Qu, Z., Tao, W., Wang, X., Wu, L., Wu, S., Xie, X. L., Tongsh, C., Huo, W., Bao, Z., et al. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.2c00539 · Cited by 274 · see citing works
Advancements and challenges of digital twins in industry
Tao, F., Zhang, H., & Zhang, C. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00603-w · Cited by 286 · see citing works
Digital Twins for Multiple Sclerosis
Voigt, I., Inojosa, H., Dillenseger, A., Haase, R., Akgün, K., & Ziemssen, T. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.669811 · Cited by 279 · see citing works
Digital Twin-enabled Collaborative Data Management for Metal Additive Manufacturing Systems
Liu, C., Le Roux, L., Körner, C., Tabaste, O., Lacan, F., & Bigot, S. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.05.010 · Cited by 252 · see citing works
The digital revolution of Earth-system science
Bauer, P., Dueben, P. D., Hoefler, T., Quintino, T., Schulthess, T. C., & Wedi, N. (2021) · Nature Computational Science · doi:10.1038/s43588-021-00023-0 · Cited by 248 · see citing works
A Socio-Technical Perspective on Urban Analytics: The Case of City-Scale Digital Twins
Nochta, T., Wan, L., Schooling, J. M., & Parlikad, A. K. (2020) · Journal of Urban Technology · doi:10.1080/10630732.2020.1798177 · Cited by 246 · see citing works
You, Z., & Feng, L. (2020) · IEEE Access · doi:10.1109/access.2020.3007206 · Cited by 230 · see citing works
Robot learning towards smart robotic manufacturing: A review
Liu, Z., Liu, Q., Xu, W., Wang, L., & Zhou, Z. (2022) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2022.102360 · Cited by 233 · see citing works
Potential and limitations of digital twins to achieve the Sustainable Development Goals
Tzachor, A., Sabri, S., Richards, C. E., Rajabifard, A., & Acuto, M. (2022) · Nature Sustainability · doi:10.1038/s41893-022-00923-7 · Cited by 224 · see citing works
Wang, B., Xie, B., Xuan, J., & Jiao, K. (2020) · Energy Conversion and Management · doi:10.1016/j.enconman.2019.112460 · Cited by 218 · see citing works
Wang, B., Zhang, G., Wang, H., Xuan, J., & Jiao, K. (2020) · Energy and AI · doi:10.1016/j.egyai.2020.100004 · Cited by 216 · see citing works
Battery Safety: Machine Learning-Based Prognostics
Zhao, J., Feng, X., Pang, Q., Fowler, M., Lian, Y., Ouyang, M., & Burke, A. F. (2024) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2023.101142 · Cited by 216 · see citing works
Digital Twins in Livestock Farming
Neethirajan, S. R., & Kemp, B. (2021) · Animals · doi:10.3390/ani11041008 · Cited by 226 · see citing works
Battery safety: Fault diagnosis from laboratory to real world
Zhao, J.-Y., Feng, X.-N., Tran, M.-K., Fowler, M., Ouyang, M., & Burke, A. F. (2024) · Journal of Power Sources · doi:10.1016/j.jpowsour.2024.234111 · Cited by 215 · see citing works
Digital twins of food process operations: the next step for food process models?
Verboven, P., Defraeye, T., Datta, A. K., & Nicolaï, B. M. (2020) · Current Opinion in Food Science · doi:10.1016/j.cofs.2020.03.002 · Cited by 213 · see citing works
A digital-twin visualized architecture for Flexible Manufacturing System
Fan, Y., Yang, J.-Z., Chen, J.-H., Hu, P., Wang, X., Xu, J.-C., & Zhou, B. (2021) · Journal of manufacturing systems · doi:10.1016/j.jmsy.2021.05.010 · Cited by 181 · see citing works
Zhuang, C., Miao, T., Liu, J., & Xiong, H. (2020) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2020.102075 · Cited by 202 · see citing works
Digital Ecosystems for Developing Digital Twins of the Earth: The Destination Earth Case
Nativi, S., Mazzetti, P., & Craglia, M. (2021) · Remote Sensing · doi:10.3390/rs13112119 · Cited by 196 · see citing works
Impactful Digital Twin in the Healthcare Revolution
Hassani, H., Huang, X., & MacFeely, S. (2022) · Big Data and Cognitive Computing · doi:10.3390/bdcc6030083 · Cited by 200 · see citing works
Digitalization of Battery Manufacturing: Current Status, Challenges, and Opportunities
Ayerbe, E., Berecibar, M., Clark, S., Franco, A. A., & Ruhland, J. (2021) · Advanced Energy Materials · doi:10.1002/aenm.202102696 · Cited by 195 · see citing works
Lattanzi, L., Raffaeli, R., Peruzzini, M., & Pellicciari, M. (2021) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2021.1911003 · Cited by 205 · see citing works
Building and exploiting a Digital Twin for the management of drinking water distribution networks
Conejos, P., Alzamora, F. M., Carot, M. H., & Campos, J. C. A. (2020) · Urban Water Journal · doi:10.1080/1573062x.2020.1771382 · Cited by 197 · see citing works
Implementation for a cloud battery management system based on the CHAIN framework
Yang, S., Zhang, Z., Cao, R., Wang, M., Cheng, H., Zhang, L., Jiang, Y., Li, Y., Chen, B., Ling, H., et al. (2021) · Energy and AI · doi:10.1016/j.egyai.2021.100088 · Cited by 178 · see citing works
Ye, X., Du, J., Han, Y., Newman, G., Retchless, D. P., Zou, L., Ham, Y., & Cai, Z. (2022) · Journal of Planning Literature · doi:10.1177/08854122221137861 · Cited by 183 · see citing works
Blockchain-Based Digital Twins: Research Trends, Issues, and Future Challenges
Suhail, S., Hussain, R., Jurdak, R., Oracevic, A., Salah, K., Hong, C. S., & Matulevičius, R. (2022) · ACM Computing Surveys · doi:10.1145/3517189 · Cited by 179 · see citing works
Digital Twins: The New Frontier for Personalized Medicine?
Cellina, M. I., Cè, M., Alì, M., Irmici, G., Ibba, S., Caloro, E., Fazzini, D., Oliva, G., & Papa, S. (2023) · Applied Sciences · doi:10.3390/app13137940 · Cited by 178 · see citing works
Digital twin applications in aviation industry: A review
Xiong, M., & Wang, H. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-09717-9 · Cited by 171 · see citing works
Digital Twin for Civil Engineering Systems: An Exploratory Review for Distributed Sensing Updating
Bado, M. F., Tonelli, D., Poli, F., Zonta, D., & Casas, J. R. (2022) · Sensors · doi:10.3390/s22093168 · Cited by 158 · see citing works
In-process tool condition forecasting based on a deep learning method
Sun, H., Zhang, J., Mo, R., & Zhang, X. (2020) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2019.101924 · Cited by 157 · see citing works
How can smart technologies contribute to sustainable product lifecycle management?
Liu, Y., Zhang, Y., Ren, S., Yang, M., Wang, Y., & Huisingh, D. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.119423 · Cited by 153 · see citing works
Envisioning the Future of Personalized Medicine: Role and Realities of Digital Twins
Vallée, A. (2024) · Journal of Medical Internet Research · doi:10.2196/50204 · Cited by 154 · see citing works
Concepts and applications of digital twins in healthcare and medicine
Zhang, K., Zhou, H.-Y., Baptista‐Hon, D. T., Gao, Y., Liu, X., Oermann, E. K., Xu, S. H., Jin, S., Zhang, J., Sun, Z., et al. (2024) · Patterns · doi:10.1016/j.patter.2024.101028 · Cited by 170 · see citing works
Yu, D., & He, Z. (2022) · Natural Hazards · doi:10.1007/s11069-021-05190-x · Cited by 163 · see citing works
Advanced Data Collection and Analysis in Data-Driven Manufacturing Process
Xu, K., Li, Y., Liu, C., Liu, X., Hao, X., Gao, J. X., & Maropoulos, P. (2020) · Chinese Journal of Mechanical Engineering · doi:10.1186/s10033-020-00459-x · Cited by 157 · see citing works
Xia, J., Huang, R., Chen, Z., He, G., & Li, W. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109542 · Cited by 151 · see citing works
A Comprehensive Review of Digital Twins Technology in Agriculture
Zhang, R., Zhu, H., Chang, Q., & Mao, Q. (2025) · Agriculture · doi:10.3390/agriculture15090903 · Cited by 160 · see citing works
Battery prognostics and health management from a machine learning perspective
Zhao, J., Feng, X., Pang, Q., Wang, J., Lian, Y., Ouyang, M., & Burke, A. F. (2023) · Journal of Power Sources · doi:10.1016/j.jpowsour.2023.233474 · Cited by 140 · see citing works
Sullivan, P. L., Billings, S., Hirmas, D., Li, L., Zhang, X., Ziegler, S., Murenbeeld, K., Ajami, H., Guthrie, A., Singha, K., et al. (2021) · Earth-Science Reviews · doi:10.1016/j.earscirev.2021.103873 · Cited by 105 · see citing works
makeTwin: A reference architecture for digital twin software platform
Tao, F., Sun, X., Cheng, J., Zhu, Y., Liu, W., Wang, Y., Xu, H., Hu, T., Liu, X., Liu, T., et al. (2023) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2023.05.002 · Cited by 142 · see citing works
Hasan, M., & Starly, B. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.05.017 · Cited by 135 · see citing works
Digital twins in safety analysis, risk assessment and emergency management
Zio, E., & Miqueles, L. (2024) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2024.110040 · Cited by 148 · see citing works
Zeb, S., Mahmood, A., Hassan, S. A., Piran, M. J., Gidlund, M., & Guizani, M. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2021.103309 · Cited by 132 · see citing works
Trustworthy digital twins in the industrial internet of things with blockchain
Suhail, S., Hussain, R., Jurdak, R., & Hong, C. S. (2022) · IEEE Internet Computing · doi:10.1109/mic.2021.3059320 · Cited by 154 · see citing works
MetaOmniCity: Toward Immersive Urban Metaverse Cyberspaces Using Smart City Digital Twins
Kuru, K. (2023) · IEEE Access · doi:10.1109/access.2023.3272890 · Cited by 129 · see citing works
Using a digital twin to explore water infrastructure impacts during the COVID-19 pandemic
Pesantez, J. E., Alghamdi, F., Sabu, S., Mahinthakumar, G., & Berglund, E. Z. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103520 · Cited by 127 · see citing works
Prognostics and health management for predictive maintenance: A review
Huang, C., Bu, S., Lee, H. H., Chan, C.-H., Kong, S. W., & Yung, W. K. C. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.05.021 · Cited by 135 · see citing works
Digital twin-enabled Graduation Intelligent Manufacturing System for fixed-position assembly islands
Guo, D., Zhong, R. Y., Lin, P., Lyu, Z., Rong, Y. K., & Huang, G. Q. (2019) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2019.101917 · Cited by 125 · see citing works
Schwartz, S. M., Wildenhaus, K. J., Bucher, A., & Byrd, B. (2020) · Frontiers in Computer Science · doi:10.3389/fcomp.2020.00031 · Cited by 118 · see citing works
BlockNet: Beyond reliable spatial Digital Twins to Parallel Metaverse
Lv, Z., Qiao, L., Li, Y., Yuan, Y., & Wang, F. (2022) · Patterns · doi:10.1016/j.patter.2022.100468 · Cited by 118 · see citing works
Digital Twin in the Maritime Domain: A Review and Emerging Trends
Madusanka, N. S., Fan, Y., Yang, S., & Xiang, X. (2023) · Journal of Marine Science and Engineering · doi:10.3390/jmse11051021 · Cited by 125 · see citing works
Digital twin model of gas turbine and its application in warning of performance fault
Hu, M., Ya, H., Lin, X., Ziyuan, L. U., Jiang, Z., & Bo, M. A. (2022) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2022.07.021 · Cited by 113 · see citing works
Sharifi, A., Beris, A. T., Javidi, A. S., Nouri, M., Lonbar, A. G., & Ahmadi, M. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102485 · Cited by 111 · see citing works
From Platform to Knowledge Graph: Evolution of Laboratory Automation
Bai, J., Cao, L., Mosbach, S., Akroyd, J., Lapkin, A. A., & Kraft, M. (2022) · JACS Au · doi:10.1021/jacsau.1c00438 · Cited by 110 · see citing works
Ricci, A., Croatti, A., Mariani, S., Montagna, S., & Picone, M. (2022) · ACM Transactions on Internet Technology · doi:10.1145/3507909 · Cited by 114 · see citing works
Chen, H., Zhang, Z., Καραμανάκος, Π., & Rodríguez, J. (2022) · IEEE Industrial Electronics Magazine · doi:10.1109/mie.2022.3216719 · Cited by 112 · see citing works
Building the metaverse using digital twins at all scales,states, and relations
Lv, Z., Xie, S., Li, Y., Hossain, M. S., & Saddik, A. E. (2022) · Virtual Reality & Intelligent Hardware · doi:10.1016/j.vrih.2022.06.005 · Cited by 108 · see citing works
Digital twin data: methods and key technologies
Zhang, M., Tao, F., Huang, B., Liu, A., Wang, L., Anwer, N., & Nee, A. Y. C. (2022) · Digital Twin · doi:10.12688/digitaltwin.17467.2 · Cited by 105 · see citing works
Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directions
Stadtmann, F., Rasheed, A., Kvamsdal, T., Johannessen, K., San, O., Kölle, K., Tande, J., Barstad, I., Benhamou, A., Brathaug, T., et al. (2023) · IEEE Access · doi:10.1109/access.2023.3321320 · Cited by 83 · see citing works
Lin, Y., Chen, L., Ali, A., Nugent, C., Cleland, I., Li, R., Ding, J., & Ning, H. (2024) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-024-00691-z · Cited by 112 · see citing works
An update method for digital twin multi-dimension models
Zhang, H., Qi, Q., Ji, W., & Tao, F. (2022) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2022.102481 · Cited by 103 · see citing works
Digital twins: dynamic model-data fusion for ecology
de Koning, K., Broekhuijsen, J., Kühn, I., Ovaskainen, O., Taubert, F., Endresen, D., Schigel, D., & Grimm, V. (2023) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2023.04.010 · Cited by 106 · see citing works
Lv, Z., & Xie, S. (2024) · Digital Twin · doi:10.12688/digitaltwin.17524.2 · Cited by 112 · see citing works
Construction method of shop-floor digital twin based on MBSE
Liu, J., Liu, J., Zhuang, C., Liu, Z., & Miao, T. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.05.004 · Cited by 98 · see citing works
Secure sharing of big digital twin data for smart manufacturing based on blockchain
Shen, W., Hu, T., Zhang, C., & Ma, S. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.09.014 · Cited by 100 · see citing works
Artificial intelligence-driven real-world battery diagnostics
Zhao, J., Qu, X., Wu, Y., Fowler, M. W., & Burke, A. F. (2024) · Energy and AI · doi:10.1016/j.egyai.2024.100419 · Cited by 104 · see citing works
A modular computational framework for medical digital twins
Masison, J., Beezley, J., Mei, Y., Ribeiro, H., Knapp, A. C., Sordo Vieira, L., Adhikari, B., Scindia, Y., Grauer, M., Helba, B., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2024287118 · Cited by 98 · see citing works
Cloud-Based Artificial Intelligence Framework for Battery Management System
Shi, D., Zhao, J., Eze, C., Wang, Z., Wang, J., Lian, Y., & Burke, A. F. (2023) · Energies · doi:10.3390/en16114403 · Cited by 96 · see citing works
Future Developments in Geographical Agent‐Based Models: Challenges and Opportunities
Heppenstall, A. J., Crooks, A. T., Malleson, N., Manley, E., Ge, J., & Batty, M. (2020) · Geographical Analysis · doi:10.1111/gean.12267 · Cited by 95 · see citing works
Survey of integrated flexible job shop scheduling problems
Li, X., Guo, X., Tang, H., Wu, R., Wang, L., Pang, S., Liu, Z., Xu, W., & Li, X. (2022) · Computers & Industrial Engineering · doi:10.1016/j.cie.2022.108786 · Cited by 95 · see citing works
Gao, X., Liu, X., He, R., Wang, M., Xie, W., Brandon, N. P., Wu, B., Ling, H., & Yang, S. (2021) · Energy storage materials · doi:10.1016/j.ensm.2021.01.007 · Cited by 93 · see citing works
A digital thread-driven distributed collaboration mechanism between digital twin manufacturing units
Liu, S., Lu, Y., Shen, X., & Bao, J. (2023) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2023.02.014 · Cited by 96 · see citing works
Advancements in remote sensing for active fire detection: A review of datasets and methods
Yang, S., Huang, Q., & Yu, M. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.173273 · Cited by 94 · see citing works
Shoji, K., Schudel, S., Onwude, D. I., Shrivastava, C., & Defraeye, T. (2021) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2021.105914 · Cited by 89 · see citing works
Generative AI Empowered Network Digital Twins: Architecture, Technologies, and Applications
Li, T., Long, Q., Chai, H., Zhang, S., Jiang, F., Liu, H., Huang, W., Jin, D., & Li, Y. (2025) · ACM Computing Surveys · doi:10.1145/3711682 · Cited by 95 · see citing works
From virtual to reality: innovative practices of digital twins in tumor therapy
Shen, S., Qi, W., Liu, X., Zeng, J., Li, S., Zhu, X., Dong, C., Wang, B., Shi, Y., Yao, J., et al. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-06371-z · Cited by 88 · see citing works
Behavior-Based Formation Control Digital Twin for Multi-AUG in Edge Computing
Wen, J., Yang, J., Li, Y., He, J., Li, Z., & Song, H. H. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3198818 · Cited by 87 · see citing works
Towards big data driven construction industry
Li, F., Laili, Y., Chen, X., Lou, Y., Wang, C., Yang, H., Gao, X., & Han, H. (2023) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2023.100483 · Cited by 97 · see citing works
Sánchez-Vaquerizo, J. A. (2021) · ISPRS Int. J. Geo Inf. · doi:10.3390/ijgi11010024 · Cited by 81 · see citing works
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Xia, J., Chen, Z., Chen, J., He, G., Huang, R., & Li, W. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.107848 · Cited by 88 · see citing works
Digital twin enhanced fault prediction for the autoclave with insufficient data
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Liu, S., Lu, Y., Li, J., Shen, X., Sun, X., & Bao, J. (2022) · Computers & Industrial Engineering · doi:10.1016/j.cie.2022.108827 · Cited by 84 · see citing works
Digital Twin Applications in the Water Sector: A Review
Bam, P. G., Rezaei, N., Roubanis, A., Austin, D. M., Austin, E. S., Tarroja, B., Takacs, I., Villez, K., & Rosso, D. (2025) · Water · doi:10.3390/w17202957 · Cited by 98 · see citing works
Machining process-oriented monitoring method based on digital twin via augmented reality
Liu, S., Lu, S., Li, J., Sun, X., Lu, Y., & Bao, J. (2021) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-021-06838-5 · Cited by 80 · see citing works
- Authors
- Tao, F., & Qi, Q.
- Affiliations
- Beihang University (CN)
- Corresponding author
- Tao, Fei
- Qi, Qinglin
- ORCID
- Venue
- Nature
- Published
- 2019-09-25
- Volume
- 573
- Issue
- 7775
- Pages
- 490-491
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2976301483
- Cited by
- 799
- References
- 2
- Citations by year
- 2026: 108 · 2025: 174 · 2024: 165 · 2023: 132 · 2022: 112 · 2021: 73 · 2020: 30 · 2019: 5
- FWCI
- 40.59
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature (publishedVersion, other-oa)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Manufacturing Process and Optimization
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Flexible and Reconfigurable Manufacturing Systems
- Keywords
- digital twin, virtual models, smart manufacturing, decision simulation, optimization, product design, manufacturing operations
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