The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities

Support-vector networks

Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421

The WEKA data mining software: an update

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750

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

Caffe: Convolutional Architecture for Fast Feature Embedding

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077

Identity mappings in deep residual networks

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741

Playing Atari with Deep Reinforcement Learning

Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471

An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression

Altman, N. (1992) · The American Statistician · doi:10.1080/00031305.1992.10475879 · Cited by 5,657

Fuzzy logic in control systems: fuzzy logic controller. I

Lee, C. C. (1990) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.52551 · Cited by 5,630

Extreme Learning Machine for Regression and Multiclass Classification

Huang, G.-B., Zhou, H., Ding, X., & Zhang, R. (2011) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2011.2168604 · Cited by 5,575

Systematic Literature Reviews in Software Engineering—A Systematic Literature Review

Kitchenham, B., Pearl Brereton, O., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009) · Information and Software Technology · doi:10.1016/j.infsof.2008.09.009 · Cited by 4,537

Network Digital Twin Toward Networking, Telecommunications, and Traffic Engineering: A Survey

Poorzare, R., Kanellopoulos, D. N., Sharma, V. K., Dalapati, P., & Waldhorst, O. P. (2025) · IEEE Access · doi:10.1109/access.2025.3531947 · Cited by 40

A roadmap for model-based bioprocess development

Mu'azzam, K., da Silva, F. V. S., Murtagh, J., & Sousa-Gallagher, M. J. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108378 · Cited by 40

Smart Master Production Schedule for the Supply Chain: A Conceptual Framework

Serrano-Ruiz, J. C., Mula, J., & Poler, R. (2021) · Computers · doi:10.3390/computers10120156 · Cited by 46

Data science and machine learning in the IIoT concepts of power plants

Milić, S., Đurović, Ž., & Stojanović, M. D. (2022) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2022.108711 · Cited by 38

Responsible cognitive digital clones as decision-makers: a design science research study

Golovianko, M., Gryshko, S., Terziyan, V. Y., & Tuunanen, T. (2022) · European Journal of Information Systems · doi:10.1080/0960085x.2022.2073278 · Cited by 43

The Anatomy of the Internet of Digital Twins: A Symbiosis of Agent and Digital Twin Paradigms Enhancing Resilience (Not Only) in Manufacturing Environments

Lehmann, J., Lober, A., Häußermann, T. M., Rache, A., Ollinger, L., Baumgärtel, H., & Reichwald, J. (2023) · Machines · doi:10.3390/machines11050504 · Cited by 43

Digital twins and AI transforming healthcare systems through innovation and data-driven decision making

Oulefki, A., Amira, A., & Foufou, S. (2025) · Health and Technology · doi:10.1007/s12553-025-00947-x · Cited by 36

Real‐time monitoring of tunnel structures using digital twin and artificial intelligence: A short overview

Afrazi, M., Armaghani, D. J., Afrazi, H., Fattahi, H., & Samui, P. (2025) · Deep Underground Science and Engineering · doi:10.1002/dug2.70029 · Cited by 36

Artificial intelligence in materials science and engineering: Current landscape, key challenges, and future trajectories

Peivaste, I., Belouettar, S., Mercuri, F., Fantuzzi, N., Dehghani, H., Izadi, R., Ibrahim, H., Lengiewicz, J., Belouettar-Mathis, M., Bendine, K., et al. (2025) · Composite Structures · doi:10.1016/j.compstruct.2025.119419 · Cited by 51

Digital Transformation and Enterprise Sustainability: The Moderating Role of Regional Virtual Agglomeration

Junping, Y., Wu, R., & Yang, H. (2023) · Sustainability · doi:10.3390/su15097597 · Cited by 39

Continuous downstream processing

Jungbauer, A., Satzer, P., Dürauer, A., Azevedo, A. M., Aires-Barros, M. R., Nilsson, B., Farid, S., Goldrick, S., Ottens, M., Sponchioni, M., et al. (2024) · Separation and Purification Technology · doi:10.1016/j.seppur.2024.126439 · Cited by 33

Driving sustainability - The role of digital twin in enhancing battery performance for electric vehicles

Rajesh, P. K., Soundarya, T., & Jithin, K. V. (2024) · Journal of Power Sources · doi:10.1016/j.jpowsour.2024.234464 · Cited by 35

CNC Machine Tool Fault Diagnosis Integrated Rescheduling Approach Supported by Digital Twin-Driven Interaction and Cooperation Framework

Liu, J., Yu, D., Hu, Y., Yu, H., He, W., & Zhang, L. (2021) · IEEE Access · doi:10.1109/access.2021.3106797 · Cited by 33

Toward a Practical Digital Twin Platform Tailored to the Requirements of Industrial Energy Systems

Kasper, L., Birkelbach, F., Schwarzmayr, P., Steindl, G., Ramsauer, D., & Hofmann, R. (2022) · Applied Sciences · doi:10.3390/app12146981 · Cited by 33

Novel statistical time series data augmentation and machine learning based classification of unobtrusive respiration data for respiration Digital Twin model

Khan, S., Alzaabi, A., Ratnarajah, T., & Arslan, T. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107825 · Cited by 34

Review on Fault Diagnosis and Fault-Tolerant Control Scheme for Robotic Manipulators: Recent Advances in Ai, Machine Learning, and Digital TwinPreprint

Quamar, M. M., & Nasir, A. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4827147 · Cited by 32

Use Cases for Digital Twin

Shah, I. A., Sial, Q., Jhanjhi, N. Z., & Gaur, L. (2022) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-6684-5925-6.ch007 · Cited by 32

Research progress and prospect of digital twin in bridge engineering

Yang, Y., Zhu, Y., & Cai, C. S. S. (2023) · Advances in Structural Engineering · doi:10.1177/13694332231218764 · Cited by 34

Digital Twins in Critical Infrastructure

Λαμπρόπουλος, Γ., Larrucea, X., & Colomo‐Palacios, R. (2024) · Information · doi:10.3390/info15080454 · Cited by 40

Recent Advances in a Medical Domain Metaverse: Status, Challenges, and Perspective

Mejia, J. M. R., & Rawat, D. B. (2022) · International Conference on Ubiquitous and Future Networks · doi:10.1109/icufn55119.2022.9829645 · Cited by 31

Virtual testbed for development and evaluation of power system digital twins and their applications

Shen, Z., Arraño-Vargas, F., & Konstantinou, G. S. (2024) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2024.101331 · Cited by 32

Digital twin technology in smart cities: A step toward intelligent urban management

Yessef, M., Hakam, Y., Tabaa, M., Alammar, M. M., & Elbarbary, Z. M. S. (2025) · Energy Reports · doi:10.1016/j.egyr.2025.11.097 · Cited by 43

Industrial applications of digital twins: A systematic investigation based on bibliometric analysis

Ren, J., Ahmad, R., Li, D., Ma, Y., & Hui, J. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103264 · Cited by 32

The Role of the IoT and Digital Twin in the Healthcare Digitalization Process

Shah, I. A., Sial, Q., Jhanjhi, N. Z., & Gaur, L. (2022) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-6684-5925-6.ch002 · Cited by 29

Perspectives on Corrosion Studies Using Scanning Electrochemical Cell Microscopy: Challenges and Opportunities

Lai, Z., Liu, M., Bi, P. Y., Huang, F., & Jin, Y. J. (2023) · Analytical Chemistry · doi:10.1021/acs.analchem.3c02423 · Cited by 30

A Review of Digital Twinning for Rotating Machinery

Inturi, V., Ghosh, B., Sabareesh, G. R., & Pakrashi, V. (2024) · Sensors · doi:10.3390/s24155002 · Cited by 33

Building Resilient Smart Cities: The Role of Digital Twins and Generative AI in Disaster Management Strategy

Razavi, H., Titidezh, O., Asgary, A., & Bonakdari, H. (2024) · Urban sustainability · doi:10.1007/978-981-97-8483-7_5 · Cited by 30

Real-to-sim: automatic simulation model generation for a digital twin in semiconductor manufacturing

Behrendt, S., Altenmüller, T., May, M., Kuhnle, A., & Lanza, G. (2025) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-025-02572-x · Cited by 33

Harnessing Digital Twin Technology for Adaptive Traffic Signal Control: Improving Signalized Intersection Performance and User Satisfaction

Dasgupta, S., Rahman, M., & Jon, S. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3420439 · Cited by 33

Digital Twins and Cyber-Physical Systems: A New Frontier in Computer Modeling

Vidyalakshmi, G. V., Gopikrishnan, S., Boulila, W., Koubâa, A., & Srivastava, G. (2025) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2025.057788 · Cited by 34

Digital Twin in Intelligent Transportation Systems: a Review

Ali, W. A., Roccotelli, M., & Fanti, M. P. (2022) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit55151.2022.9804017 · Cited by 31

A Standardized Data Model for the Battery Passport: Paving the Way for Sustainable Battery Management

Gianvincenzi, M., Marconi, M., Mosconi, E. M., & Tola, F. (2024) · Procedia CIRP · doi:10.1016/j.procir.2024.01.014 · Cited by 26

NIR spectroscopy—CNN‐enabled chemometrics for multianalyte monitoring in microbial fermentation

Banerjee, S., Mandal, S., Jesubalan, N. G., Jain, R., & Rathore, A. S. (2024) · Biotechnology and Bioengineering · doi:10.1002/bit.28681 · Cited by 26

Extending X-reality technologies to digital twin in cultural heritage risk management: a comparative evaluation from the perspective of situation awareness

Guo, Y., Liu, L., Huang, W., Shen, M., Yi, X., Zhang, J., & Lu, S. (2024) · Heritage Science · doi:10.1186/s40494-024-01363-6 · Cited by 34

AI-Enhanced Digital Twins for Energy Efficiency and Carbon Footprint Reduction in Smart City Infrastructure

Li, Y. (2025) · Applied and Computational Engineering · doi:10.54254/2755-2721/2025.20569 · Cited by 25

Digital twins for smart asset management in the energy industry: State-of-the-art

Amin, U., Kim, D., Ahmed, F. N., Ahmad, G., & Hossain, M. J. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128358 · Cited by 28

A data-driven digital twin model for bridge health monitoring using feature fusion and unsupervised deep learning

Mousavi, V., Rashidi, M., Ghazimoghadam, S., & Samali, B. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103534 · Cited by 31

Deep Learning-Empowered Clinical Big Data Analytics in Healthcare Digital Twins

Lv, Z., Guo, J., & Lv, H. (2023) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2023.3252668 · Cited by 25

Sustainability of Methods for Augmented Ultra-Precision Machining

Lee, Y. J., & Wang, H. (2023) · International Journal of Precision Engineering and Manufacturing-Green Technology · doi:10.1007/s40684-023-00546-z · Cited by 25

Bridging cybersecurity with digital twin technology: a thematic analysis

Achuthan, K., Gupta, B. B., & Raghu Raman, R. (2025) · International Journal of Information Security · doi:10.1007/s10207-025-01115-y · Cited by 29

Using UML and OCL Models to Realize High-Level Digital Twins

Muñoz, P., Troya, J., & Vallecillo, A. (2021) · ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C) · doi:10.1109/models-c53483.2021.00037 · Cited by 24

Digital Twin-Enabled Infrastructures: A Bibliometric Analysis-Based Review

Taherkhani, R., Ashtari, M. A., & Aziminezhad, M. (2023) · Journal of Infrastructure Systems · doi:10.1061/jitse4.iseng-2323 · Cited by 25

Digital twin technology in oil and gas infrastructure: Policy requirements and implementation strategies

Esiri, A. E., Sofoluwe, O. O., & Ukato, A. (2024) · Engineering Science & Technology Journal · doi:10.51594/estj.v5i6.1221 · Cited by 24

Digital twin in healthcare: Classification and typology of models based on hierarchy, application, and maturity

Maïzi, Y., Arcand, A., & Bendavid, Y. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101379 · Cited by 23

A Survey on Digital Twins: Enabling Technologies, Use Cases, Application, Open Issues, and More

Hassija, V., Chamola, V., De, R., Das, S., Chakrabarti, A., Sangwan, K. S., & Pandey, A. (2024) · IEEE Journal of Selected Areas in Sensors · doi:10.1109/jsas.2024.3523856 · Cited by 23

Sustainable Intelligent Transportation Systems via Digital Twins: A Contextualized Survey

Martínez, V. M. G., Campelo, D. R. D. S., & Ribeiro, M. R. N. (2025) · IEEE Open Journal of Intelligent Transportation Systems · doi:10.1109/ojits.2025.3553696 · Cited by 24

The Future of Digital Twin Research and Development

Bossuyt, D. L. V., Allaire, D., Bickford, J., Bozada, T. A., Chen, W., Cutitta, R. P., Cuzner, R., Fletcher, K., Giachetti, R. E., Hale, B., et al. (2025) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4068082 · Cited by 21

Characterizing the Role of Geospatial Science in Digital Twins

Metcalfe, J. B., Ellul, C., Morley, J. G., & Stoter, J. (2024) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi13090320 · Cited by 26

Digital twins and the ethics of health decision-making concerning children

Braun, M., & Krutzinna, J. I. (2022) · Patterns · doi:10.1016/j.patter.2022.100469 · Cited by 22

Knowledge Integration in Smart Factories

Zenkert, J., Weber, C., Dornhöfer, M., Abu-Rasheed, H., & Fathi, M. (2021) · Encyclopedia · doi:10.3390/encyclopedia1030061 · Cited by 23

A Comprehensive Survey on Emerging AI Technologies for 6G Communications: Research Direction, Trends, Challenges, and Opportunities

Das, B. R., Hasan, S. R., Sabuj, S. R., Hossain, M. A., & Ray, S. K. (2025) · International Journal of Intelligent Networks · doi:10.1016/j.ijin.2025.06.001 · Cited by 35

A review on the impact of big data analytics in transforming agricultural practices, food processing, and preservation strategies

Hussein, J. B., Workneh, T. S., Kassim, A., Mugodo, K., Melesse, S. F., & El‐Mesery, H. S. (2025) · Applied Food Research · doi:10.1016/j.afres.2025.101234 · Cited by 27

Mixers Detection in bitcoin network: a step towards detecting money laundering in crypto-currencies

Rathore, M. M., Chaurasia, S. S., & Shukla, D. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020982 · Cited by 21

BIG DATA IN THE OIL SECTOR: A REVIEW OF HOW ANALYTICS IS REVOLUTIONIZING SUPPLY CHAIN OPERATIONS

Agho, M. O., Adeyinka, M. A., Okogwu, C., Ikwue, U., Ufoaro, O. A., Ayodeji, S. A., & Daraojimba, C. (2023) · Economic Growth and Environment Sustainability · doi:10.26480/egnes.02.2023.96.104 · Cited by 21

Network Digital Twins: A Systematic Review

Verdecchia, R., Scommegna, L., Picano, B., Becattini, M., & Vicario, E. (2024) · IEEE Access · doi:10.1109/access.2024.3453034 · Cited by 24

Predicting Wi‐Fi link quality through artificial neural networks

Scanzio, S., Xia, F., Cena, G., & Valenzano, A. (2021) · Internet Technology Letters · doi:10.1002/itl2.326 · Cited by 20

Smart batch process: The evolution from 1D and 2D to new 3D perspectives in the era of Big Data

Zhou, Y., & Gao, F. (2023) · Journal of Process Control · doi:10.1016/j.jprocont.2023.103088 · Cited by 23

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

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

Transformative roles of digital twins from drug discovery to continuous manufacturing: pharmaceutical and biopharmaceutical perspectives

Maharjan, R., Kim, N. A., Kim, K. H., & Jeong, S. H. (2025) · International Journal of Pharmaceutics X · doi:10.1016/j.ijpx.2025.100409 · Cited by 30

Intelligent Agents based Integration of Machine Learning and Case Base Reasoning System

Kaswan, K. S., Dhatterwal, J. S., & Balyan, A. (2022) · International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) · doi:10.1109/icacite53722.2022.9823890 · Cited by 19

Digital twin based multi-objective energy management strategy for energy internet

Wang, D., Fan, R., Li, Y., & Sun, Q. (2023) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2023.109368 · Cited by 19

Review of Electric Vehicle Testing Procedures for Digital Twin Development: A Comprehensive Analysis

Rjabtšikov, V., Rassõlkin, A., Kudelina, K., Kallaste, A., & Vaimann, T. (2023) · Energies · doi:10.3390/en16196952 · Cited by 21

Digital Twinning and Remote Engineering for Immersive Embedded Systems Education

Hussein, R., Guo, M., Amarante, P., Rodríguez-Gil, L., & Orduña, P. (2023) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie58773.2023.10343436 · Cited by 19

Reinforcement learning for control and optimization of real buildings: Identifying and addressing implementation hurdles

Kannari, L., Wessberg, N., Hirvonen, S., Kantorovitch, J., & Paiho, S. (2025) · Journal of Building Engineering · doi:10.1016/j.jobe.2025.112283 · Cited by 18

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

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

Digital Twin Based Laboratory for Control Engineering Education

Tjahyadi, H., Prasetya, K., & Murwantara, I. M. (2023) · International Journal of Information and Education Technology · doi:10.18178/ijiet.2023.13.4.1856 · Cited by 18

Key technologies for wireless network digital twin towards smart railways

Guan, K., Guo, X., He, D., Svoboda, P., Berbineau, M., Wang, S., Ai, B., Zhong, Z. D., & Rupp, M. (2024) · High-speed Railway · doi:10.1016/j.hspr.2024.01.004 · Cited by 18

Digital Twins for Research and Innovation in Support of the European Green Deal Data Space: A Systematic Review

Otsu, K., & Masó, J. (2024) · Remote Sensing · doi:10.3390/rs16193672 · Cited by 17

Adversarial Examples on XAI-Enabled DT for Smart Healthcare Systems

Imam, N. H. (2024) · Sensors · doi:10.3390/s24216891 · Cited by 17

Leveraging Big Data and AI for Sustainable Urban Mobility Solutions

Yusuf, O. U., Rasheed, A., & Lindseth, F. (2025) · Urban Science · doi:10.3390/urbansci9080301 · Cited by 17

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

Revolutionizing sheet metal stamping through industry 5.0 digital twins: a comprehensive review

Modad, O. A. A., Ryska, J., Chehade, A., & Ayoub, G. Y. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02453-9 · Cited by 20

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

Inference of Simulation Models in Digital Twins by Reinforcement Learning

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

Role of Building Automation Technology in Creating a Smart and Sustainable Built Environment

Singh, D., & Singh, A. (2023) · Evergreen · doi:10.5109/6781101 · Cited by 16

Reshaping the Digital Twin Construct with Levels of Digital Twinning (LoDT)

Vieira, J., Martins, J. P., Almeida, N. M., Patrício, H., & Morgado, J. G. (2023) · Applied System Innovation · doi:10.3390/asi6060114 · Cited by 18

Digital twin challenges and opportunities for nuclear fuel manufacturing applications

Bandala, M., Chard, P. M. J., Cockbain, N., Dunphy, R. D., Eaves, D., Hutchinson, D., Lee, D., Ma, X., Marshall, S., Murray, P., et al. (2024) · Nuclear Engineering and Design · doi:10.1016/j.nucengdes.2024.113013 · Cited by 16

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

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

Digital Twinning in Smart Grid Networks: Interplay, Resource Allocation and Use Cases

Othman, A., Kaddoum, G., Evangelista, J. V. C., Au, M., & Agba, B. L. (2023) · IEEE Communications Magazine · doi:10.1109/mcom.001.2200823 · Cited by 15

Review of Digital Twin in the Automotive Industry on Products, Processes and Systems

Liu, H., Zhang, B., Wu, V. L., Yang, X. Z., & Wang, L. (2025) · International Journal of Automotive Manufacturing and Materials · doi:10.53941/ijamm.2025.100006 · Cited by 20

Digital Twins in Security Operations: State of the Art and Future Perspectives

Empl, P., Koch, D., Dietz, M., & Pernul, G. (2025) · ACM Computing Surveys · doi:10.1145/3746279 · Cited by 14

Exploring the potential of digital twins in the food industry and their role in developing food formulations

Paris, M. J., Jiménez-González, O., González-Pérez, J. E., & Ramírez‐Corona, N. (2025) · Chemical Engineering and Processing - Process Intensification · doi:10.1016/j.cep.2025.110583 · Cited by 14

Integration of Artificial Intelligence in the life cycle of industrial Digital Twins

Abdoune, F., Nouiri, M., Cardin, O., & Castagna, P. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.10.092 · Cited by 15

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

Interoperable Data Analytics Reference Architectures Empowering Digital-Twin-Aided Manufacturing

Marosi, A. C., Emődi, M., Hajnal, Á., Lovas, R., Kiss, T., Poser, V., Antony, J., Bergweiler, S., Hamzeh, H., DesLauriers, J., et al. (2022) · Future Internet · doi:10.3390/fi14040114 · Cited by 14

Artificial Intelligence-Aided Digital Twin Design: A Systematic ReviewPreprint

Hao, N., Li, Y., Liu, K., Liu, S., Lu, Y., Xu, B., Li, C., Chen, J., Yue, L., Fu, T., et al. (2024) · Preprints.org · doi:10.20944/preprints202408.2063.v1 · Cited by 16

Knowledge Graphs and Artificial Intelligence for the Implementation of Cognitive Heritage Digital Twins

Felicetti, A., Himmiche, A., & Somenzi, M. (2025) · Applied Sciences · doi:10.3390/app151810061 · Cited by 21

Industry 5.0 readiness factors for implementing digital twins in sustainable supply chains: A pathway to circularity

Tyagi, M., & Tyagi, K. (2025) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2025.146795 · Cited by 13

The impact of AI applications, information sharing, and supply chain resilience on agricultural supply chain performance

Yin, F., Lo, M. C., Mohamad, A. A., & Sin, K. Y. (2025) · Journal of Data Information and Management · doi:10.1007/s42488-025-00155-2 · Cited by 28

Concepts, key technologies, applications and development trends in autonomous transportation systems

Liu, J. M., Yu, H., Huang, A., Ma, X., Wu, B., Sun, J., Jia, L., Chen, Y., Wang, Y., Wang, J., et al. (2025) · Autonomous Transportation Research · doi:10.1016/j.atres.2025.12.002 · Cited by 13

AI-enhanced Digital Twin systems for warehouse logistics optimization: A review of challenges with solutions and future directions

Alam, M. M., Ugli, R. A. N., Tanha, K. J., & Jun, T. (2026) · ICT Express · doi:10.1016/j.icte.2026.01.009 · Cited by 18

Hybrid Models, Digital Twins, and Digital Shadows for Sustainable Membrane Technologies: A Critical Review

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