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

Digital Twins in the Metaverse and Their Application to Personalized Medicine

Malviya, R., & Rajput, S. (2025) · Wiley eBooks · doi:10.1002/9781394315680.ch5

Role of Technological Innovation in Supply Chain

Srinivasarao, L. B. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-1982-7.ch006 · Cited by 1

Towards a Digital Twin of a Decanter Centrifuge for Wastewater Management

Donato, M. C. D., Corradini, F., Francioni, N., Giovannini, F., Re, B., & Scacchia, A. (2025) · Lecture notes in business information processing · doi:10.1007/978-3-031-94931-9_15

Introducing Machine Learning: Engineering

Lee, J. (2025) · Machine Learning Engineering · doi:10.1088/3049-4761/aded84 · Cited by 2

Knowledge-Orchestrated Digital Twin Suite for Smart Refinery Operations

Seshagiri, S., Khadka, T., Chand, T., Vishwakarma, A., Malhotra, C. P., Gaduparthi, T., & Gautham, B. P. (2025) · Springer eBooks · doi:10.1007/978-3-031-89654-5_10

Design and Implementation of an Intelligent Employment Recommendation System Based on Big Data Analytics

Xu, X. (2025) · International Journal of High Speed Electronics and Systems · doi:10.1142/s0129156425403912

Research on optimization strategy and practice of university course management based on machine learning and big data analysis

He, Y. (2025) · Journal of Computational Methods in Sciences and Engineering · doi:10.1177/14727978251355807

Digital twin: A scoping review, challenges and open research

Chauhan, R., & Veena (2025) · International Journal of Research in Engineering · doi:10.33545/26648776.2025.v7.i2c.104

Augmented Reality-Enhanced Digital Twin for Automated Vehicle Testing

Khaire, R., Guo, H., Gangwar, H., Saravanan, N. K., Wishart, J. D., & Zhao, J. (2025) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2025.07.030

Distributed Resilience Assessment of Critical Infrastructures with Digital Twins Considering Uncertainty

Gebhard, T., & Tundis, A. (2025) · International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) · doi:10.1109/dcoss-iot65416.2025.00150

أمكانية تطبيق التوأمة الرقمية لتحسين الأداء التنظيمي

القصير, ن. أ., & أرحيم, ع. ع. (2025) · Journal of Kufa Studies Center · doi:10.36322/jksc.177(b).20395

Digital Twin-Supported BESS Decision Support of RUL-Based Maintenance

Zhao, K., Liu, Y., Ming, W., Zhou, Y., & Wu, J. (2025) · IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) · doi:10.1109/ice/itmc65658.2025.11106621

The use of smart glasses in the healthcare metaverse: A systematic review

Malungana, L., & Chimbo, B. (2025) · Suid-Afrikaanse Tydskrif vir Natuurwetenskap en Tegnologie · doi:10.36303/satnt.2025.44.1.548e · Cited by 1

How Small and Medium Entreprises are Leveraging Artificial Intelligence: A Dematel Approach

Mulongo, N. Y. (2025) · International Conference on Smart Applications, Communications and Networking (SmartNets) · doi:10.1109/smartnets65254.2025.11106914

96‐2: Distinguished Student Paper: Optimize Manufacturing Operations with Digital Twin and Deep Q‐Network

Park, S., Lee, S., Son, H., Kim, J.-H., Han, D., Lim, J., Choi, E., Jo, S., Lee, H. S., Lee, W., et al. (2025) · SID Symposium Digest of Technical Papers · doi:10.1002/sdtp.18431

Introductory Chapter: Artificial Intelligence, Big Data, and New Trends in Quality Control

Qamar, S. Z. (2025) · Industrial engineering and management. · doi:10.5772/intechopen.1011680

Futuristic Development in Health Care Using Digital Twin Technology and AIoT

Yadao, S., & Anisha, P. R. (2025) · Information systems engineering and management · doi:10.1007/978-3-031-76286-4_2

Leveraging Big Data and AI for Improved Energy Efficiency in MENA Utilities: A Framework for Consumption Management and Operational Insights

Shaat, M., & Mukala, P. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-9248-4_11

Optimizing Collaborative Learning Through Advanced Grouping Techniques: A Comparative Analysis of ML-KNN and Genetic Algorithms

Khoual, M., Mcharfi, Z., Elkaimbillah, Z., & Asri, B. E. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-01536-5_19 · Cited by 1

Big Data in Procurement 4.0 – Digitalizing and Integrating Procurement Function

Matha, R., & Panigrahi, R. R. (2025) · Wiley eBooks · doi:10.1002/9781394238545.ch7

The Digital Pivot: A Systematic Review of Digital Technology's Impact on Manufacturing and Future Research Agenda

Yan, C. (2025) · International Journal of Multidisciplinary Research · doi:10.65231/ijmr.v1i1.24

Digital Twins and Extended Reality for AI-Infused Metaverse Solutions for Smart Healthcare System

Adeniyi, A. E., Awotunde, J. B., Arulogun, O. T., Aworinde, H. O., Ubaru, S., & Alaebo, C. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-99946-8_17

Analysis of the Central Limit Theorem-Driven Numerosity Reduction Technique: Implications for Binary Classification Algorithm Performance and Training Duration

Azman, A., Husin, H. S., Hamzah, N., Phang, S. K., Yogarayan, S., & Lalitha, R. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-7253-0_39

Adversarial-Based Probabilistic Manifold Anomaly Detection Model for Mechanical Fault Diagnosis

Chen, S., Leyi, Z., & Xi, T. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228118

The Essence Of Artificial Intelligence In the Manufacturing Companies

Mulongo, N. Y., & Bejaoui, T. (2025) · International Symposium on Networks, Computers and Communications (ISNCC) · doi:10.1109/isncc66965.2025.11250511

Vibrational spectroscopic methods combined with chemometrics for phytochemical analysis

Shawky, E. M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33710-9.00013-3

Fault Diagnosis and Prediction of Electric Vehicle Drive using Support Vector Machine Algorithm

Kumar, M. P., Velpula, S., Saiprakash, C., & Sahoo, B. (2025) · IEEE International Conference on Sustainable Energy and Future Electric Transportation (SEFET) · doi:10.1109/sefet65155.2025.11255357

A Framework for Managing Non-Emergency Civic Services Based on Digital Twinning and Crowdsourcing

Zhang, J., Ren, K., Jing, Y., & Zhang, A. (2025) · ASCE International Conference on Computing in Civil Engineering (i3CE) · doi:10.1061/9780784486122.058

Federated Digital Twin Framework for Resilient Substation Reconfiguration in UK Distribution Networks under EV Growth and Net-Zero TargetsPreprint

ASTAN, S., Hajizadeh, A., & Fatehi, K. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5939057

How Does Augmented Intelligence Affects The South African Manufacturing Firms

Mulongo, N. Y. (2025) · International Conference on Cyber Resilience (ICCR) · doi:10.1109/iccr67387.2025.11291660

Digital Twin Temporal Resource Allocation and Control Framework for IoT Networks Using TD3-LSTM

Baggam, D. S., & Rani, S. (2026) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2026.3653432

An Iterative Lifecycle Model for Engineering and Operating Digital Twins

Klaassen, C., Steindl, G., & Hofmann, R. (2026) · IEEE Open Journal of the Industrial Electronics Society · doi:10.1109/ojies.2026.3654869

Cognitive Digital Twins: Integrating Deep Learning Models for Proactive Facilities Management in Smart Buildings

Elias, C., & Issa, R. R. A. (2026) · ASCE International Conference on Computing in Civil Engineering (i3CE) · doi:10.1061/9780784486436.040

Applying the Digital Product Passport for Enhanced Lifecycle Management of Electrical and Electronic Equipment: A Case Study of a Washing Machine

Pietroni, G., Gianvincenzi, M., Marconi, M., & Favi, C. (2026) · Lecture notes in mechanical engineering · doi:10.1007/978-3-032-14953-4_17

FusionNet+: A Multi-Modal Deep Ensemble Architecture for Predicting Natural Disaster Severity and Economic Loss

Islam, S., Arman, R. A., Hossain, M., Simul, M. K. K., Ullah, A., & Akter, S. (2025) · IEEE International Conference on Computing, Applications and Systems (COMPAS) · doi:10.1109/compas67506.2025.11381628

AI-Driven Solutions for Predictive Aircraft Maintenance using Sensor Data

Kalsani, S. K., Sudhakar, G., Rajeswari, S., Jelli, K., Kumar, K. S., & Rajesh, P. (2025) · International Conference on Electronics, Communication and Aerospace Technology (ICECA) · doi:10.1109/iceca66444.2025.11383516

Improving the Design and Implementation of Digital Twins with Model-Driven Software Development

Barriga, A., Barriga, J. A., Pérez, M. A., & Clemente, P. J. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-10929-3_30

Enhancing user authentication and security in metaverse: a review of innovative techniques and the role of zero-trust models

Ethiraj, J., & Ellappan, V. (2026) · Frontiers in Virtual Reality · doi:10.3389/frvir.2026.1753188

Advances in AI-Driven Big Data Technologies: A Five-Year Review

Chen, C., Luo, Q., & Yang, L. (2025) · International Conference on Big Data and Information Analytics (BigDIA) · doi:10.1109/bigdia68682.2025.11382836

Integrating Machine Learning, Deep Learning, Iot, Ai, and Data Science for Future Innovations

Megha, & Srivastava, V. K. (2025) · International Conference on Innovations and Emerging Technologies in AI & Communication Systems (IETACS) · doi:10.1109/ietacs68750.2025.11385644

From PC to AI Through Digital Twins - A glance into the Race for Technological Ideology

Sychev, S., Batako, A. D. L., Antosz, K., Trung, N. T., Xavior, M. A., & Butdee, S. (2026) · EPJ Web of Conferences · doi:10.1051/epjconf/202635404001

Data-driven quantification of fecal and total coliform bacteria for digital-twin-assisted water-quality monitoring

Barriga, A., Elipe, G. T., Barriga, J. A., & Clemente, P. J. (2026) · Array · doi:10.1016/j.array.2026.100745

Auto-Reconfigurable Storage Structure to Minimize Read Access Time of Time-Series Data in Digital Twins

Onishi, T., & Kanemasa, Y. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11401787

A deep learning-based digital twin system for creep behaviour of metallic alloys

Zhao, P., Liu, Y., Zhang, J., Zhang, H., & Xuan, F. (2026) · Materials at High Temperatures · doi:10.1080/09603409.2026.2643937

Intelligent framework for battery parameter estimation using vehicle runtime log data towards battery digital twin

Sharma, S. K., & Sengupta, S. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.124173

Network Traffic Data Super-Resolution for Digital Twin Network Using Cross-Attention SRGAN

Tang, X. H., Che, C., Liu, Y., Hu, J., & Yang, K. (2025) · Globecom · doi:10.1109/globecom59602.2025.11431988

Cybersecurity issues related to incorporation of VR components into Industry 5.0 human-machine interfaces

Kozak, R., Skorenkyy, Y., Kramar, O., Brevus, V., & Zagorodna, N. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.02.022

Unraveling role of artificial intelligence capability through digital twin practices, lean six sigma and total quality management for smart manufacturing performance

Dehan, M. F. Z., Disha, J. F., & Anzum, K. M. T. (2026) · International Journal of Lean Six Sigma · doi:10.1108/ijlss-09-2025-0251

Digital twin utilization in photovoltaic energy systems: Challenges and opportunities

Alao, K. T., Gilani, S. I. U. H., Sopian, K. K., Anshory, I., Jamaaluddin, Alao, T. O., & Sulliev, A. K. (2026) · AIP conference proceedings · doi:10.1063/5.0318798

Fast prediction of oceanic nearshore circulation (FPOCH) system with multi-model fusion strategies

Feng, X., Jiang, Y., Huang, H., Hu, Z., Liu, M., Gao, S., Pei, S., Zheng, G., & Feng, W. (2026) · Applied Ocean Research · doi:10.1016/j.apor.2026.105059

Navigating Big Data: From Volume to Value in Next‐Gen Recommendation Systems

Balasubramanian, N., Ruba, A., Rajalingam, B., & Manjula, A. (2026) · Wiley eBooks · doi:10.1002/9781394351572.ch22

317Machine Learning in Smart Cities: Leveraging Particle Swarm Optimization (PSO) and Decision Trees for Urban Development Planning

Legapriyadharshini, N., Nanthini, S., Akila, A., Suganya, A., Nisha, N., & Kumari, V. S. (2025) · De Gruyter eBooks · doi:10.1515/9783112221082-027

Trust Management for Digital Twins in Cloud-Based Environments

Azzedin, F., Alhazmi, T. A., & Hammoudeh, M. (2025) · International Conference on Future Networks and Distributed Systems (ICFNDS) · doi:10.1145/3789692.3789851

From Digital Twins to Cognitive Enterprises

Goyal, A., Rajendran, V., & Dhaundiyal, P. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-9998-0.ch003

A diffusion model with key node feature guidance for digital twin of rocket engine frames under variable working conditions

Chen, J., Wang, W., Liu, Z., Zheng, C., Liu, Y., & Wang, J. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.112831

Sustainability-Driven Cognitive Digital Twins for Enterprise Transformation

Bhati, N., Kataria, A., & Dhaundiyal, P. (2026) · IGI Global eBooks · doi:10.4018/979-8-2600-1669-5.ch014

Explainable Intelligence in Digital Twins: State-of-the-Art and Open Challenges

Nguyen, L. V., & Ho, H.-T. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-95-6111-7_1

AIML-Based Multi-Objective Optimization of Energy Use and Tool Life Under Financial Constraints

Vinjamuri, D., Ramya, M., Snehalatha, P. I., Vandhana, K., Narayana, T. V., & Ammisetty, V. (2025) · IEEE International Conference on Recent Trends in Computing and Smart Mobility (RCSM) · doi:10.1109/rcsm67767.2025.11507409

Exploring the Impact of AI Consciousness on Organizational Citizenship Behavior and Counterproductive Work Behavior Within Evolving HR Business Models

Sharma, C., Gupta, N., & Som, B. K. (2026) · Palgrave studies in democracy, innovation, and entrepreneurship for growth · doi:10.1007/978-3-032-09791-0_5

Digital Twins in Geospatial Science for Urban Planning and Development

Hooda, Y., Mehta, J., & Sharma, L. (2026) · Data-intensive research · doi:10.1007/978-3-032-20311-3_6

Concerns of Using Large Language Models in Health Care Research and Practice: Umbrella Review

Yarar, F., Addis, P., Fairweather, M., Craig, D., & O’Keefe, H. (2026) · Journal of Medical Internet Research · doi:10.2196/87804 · Cited by 1

Hybrid Feature Selection–Based Approach to Predict Celiac Disease Using Ensemble Machine Learning With Hyperparameter Tuning and Model Explainability

Uddin, K. M. M., Bhuiyan, M. T. A., & Hassan, M. M. U. (2026) · Journal of Electrical and Computer Engineering · doi:10.1155/jece/6024336

Digital Twin Models for Space Robotics: Objectives, Applications, and Challenges

Rostamnia, M., Pettinari, S., & Pelliccione, P. (2026) · Array · doi:10.1016/j.array.2026.100902

Introduction to Prototyping: Definitions, Concepts and Enabling Technologies

Kabir, M. R., & Ray, S. (2026) · Synthesis lectures on engineering, science, and technology · doi:10.1007/978-3-032-18938-7_1

Digital Twin Tools for Smart Manufacturing

Kabir, M. R., & Ray, S. (2026) · Synthesis lectures on engineering, science, and technology · doi:10.1007/978-3-032-18938-7_5

From efficiency tool to system enabler: An evolutionary study of AI-driven transformation in smart manufacturing systems

Xie, W., Yu, J., Chen, R., Li, Z., & Zhen, D. (2026) · International Journal of Production Economics · doi:10.1016/j.ijpe.2026.110097

An Improved Transformer-based Fault Diagnosis Methodology in Digital Twin System

He, J., Zhang, Y., Zuo, Y., & Tao, F. (2026) · Procedia CIRP · doi:10.1016/j.procir.2026.05.215

Evolution of digital twin technology: future trends in artificial intelligence

Odiaga, G. A., & Nyangaresi, V. O. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-43896-7.00006-6

Ethical considerations and challenges of artificial intelligence in digital twins

Askarizade, R., & Rafsanjani, M. K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-43896-7.00010-8

Future trends of artificial intelligence and the evolution of digital twins

Fatemidokht, H., & Rafsanjani, M. K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-43896-7.00002-9

Early childhood milestones: investigating the relationship between first tooth eruption and independent walking

Usama, M. (2026) · Bulletin of Faculty of Physical Therapy · doi:10.1186/s43161-026-00360-z

Semantic modelling of AI-integrated digital twins for enhanced transparency

Shu, Y., Hellicar, A., WANG, W., & Rahman, A. (2026) · Journal of Intelligent Information Systems · doi:10.1007/s10844-026-01065-4

Navigating Privacy in Digital Twin: A Framework for Regulatory Compliance (DTFRC)

Tolah, A., Alghamdi, O., Aleisa, N., Imam, N. H., & Alhelaly, S. (2026) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-026-11430-8

Clinical applications of digital twin technology in In Vitro Fertilisation

Olawade, D. B., Abe, O. R., Nwazuo, K. E., Apena, T., Olawuyi, O. F., & Egbon, E. (2026) · Journal of Gynecology Obstetrics and Human Reproduction · doi:10.1016/j.jogoh.2026.103235

The European tracking network through time: united efforts to advance aquatic conservation in Europe

Reubens, J., Aarestrup, K., Abecasis, D., Afonso, P., Alós, J., Aspillaga, E., Birnie‐Gauvin, K., Casals, D., Desmet, P., Edwards, J. E., et al. (2026) · Animal Biotelemetry · doi:10.1186/s40317-026-00475-z

Application of Modern Analytic Solutions in Various Fields of Life in the Context of Digital Transformation (in the Practice of Organizations and Research of Youth)

Khizbullina, R., Huzieva, E., & Gatina, L. I. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-20582-7_6

Digital Twins to the Rescue in Education

Veldkamp, B. P., van der Velde, M., de Vries, J., Bulder, E., Essen, A., Hakvoort, B. E., & Ottow‐Henning, E. (2026) · Springer eBooks · doi:10.1007/978-3-658-49955-6_17

Simulation-Based Digital Twin for Airline Disruption Recovery Using Synthetic Network and Passenger DataPreprint

Lad, N., Champagne, L. E., Robbins, M. J., Li, K., & Kabban, C. M. S. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7079421

Characteristics and Key Technologies of Digital Twin-Driven Manufacturing System

Liu, S., Zheng, P., & Bao, J. (2026) · Springer eBooks · doi:10.1007/978-981-95-8236-5_3

A repository for agile Digital Twins across the building life cycle

Zech, P., Pobitzer, P., Hammes, S., Fröch, G., & Breu, R. (2026) · Developments in the Built Environment · doi:10.1016/j.dibe.2026.100986

AI-Powered Digital Twins for Patient-Specific Orthopedic Surgery Planning

Bhasme, S., Chute, T., Wadibhasme, S., Mankar, M., Abhyankar, J., & Hajare, S. (2025) · International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · doi:10.1109/i-smac65734.2025.11597814

Role of Artificial Intelligence and Digital Twin Technologies in the Transition to Human-Centric Manufacturing

Zeba, G. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-30113-0_18

A Hybrid Solver for Digital Twins: Combining Symbolic and Numerical Methods

Čolo, N., Isić, S., & Džafić, I. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-30113-0_13

State of the Art of the Implementation of Artificial Intelligence in Manufacturing Sector

Mulongo, N. Y. (2026) · International Conference on Smart Applications, Communications and Networking (SmartNets) · doi:10.1109/smartnets69662.2026.11604797

Digital twins in geotechnical engineering: bridging data, models, and engineering practice

Tiwari, S., & Das, S. K. (2026) · Innovative Infrastructure Solutions · doi:10.1007/s41062-026-02846-0

Barriers and enablers for traceability and federation in model-based digital twin engineering

Berardinelli, L., Muttillo, V., Brunelière, H., Eramo, R., Bucaioni, A., Combemale, B., Ristov, S., Gritsch, P., Khelladi, D. E., Dao, D., et al. (2026) · Information and Software Technology · doi:10.1016/j.infsof.2026.108230

AI Tools for Energy Management: Revolutionising Smart Grids, Microgrids, Homes and Renewables

Ali, S. M., Khan, B., Ullah, K., Ullah, Z., Mehmood, C. A., Gulfam, S. M., & Gruosso, G. (2026) · International Journal of Energy Research · doi:10.1155/er/7310134

Model-Driven Data Contracts for Digital Twin Services

Zech, P., & Dávid, I. (2026) · ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS) · doi:10.1145/3822455.3838769

MACHINE LEARNING-ENABLED CLOUD DETECTION AND WEATHER FORECASTING: A SYSTEMATIC REVIEW FOR SUSTAINABLE ENVIRONMENT MONITORING

Dhotre, V. A., Pise, G. S., Raskar, I. R., Shinde, S., Bamane, K., & Jagtap, A. (2026) · Nigerian Journal of Technology · doi:10.67358/njt.2026.6045si

AI technologies for building energy applications: advances, opportunities, challenges and future perspectives

Xie, L., Shan, K., Tang, H., Li, H., & Wang, S. (2026) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2026.117392

DDPM-Based Dynamic Feature Embedding Digital Twin Fault Diagnosis of Rolling Bearing

Deng, J., Wen, G., Lei, Z., Xu, Q., Su, Y., Zhang, Z., & Chen, X. (2026) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4072512

Introduction to smart classrooms

Göçen, A., & Döğer, M. F. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-41632-3.00023-0

Digital twin in smart education: Challenges, limitations, and case studies

Abdalzaher, M. S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-41632-3.00032-1

Future perspectives: personalized medicine and senescence modulation in cardiovascular health

Nissar, I., Uppal, J., Gayaz, M., Chawla, A., Mohi-ud-din, R., Hussain, M. S., Mir, R. H., & Ahmad, A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45811-8.00008-8

Real-time anomaly detection using Diffusion Models in a digital twin settingPreprint

Kolovos, D., & Kalogeris, I. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7286911

Development of a Web-Based Digital Twin Module for Smart Manufacturing and Logistics

Chen, K., Kuo, Y.-J., & Oberschmidt, D. (2026) · International Conference on Control, Automation and Diagnosis (ICCAD) · doi:10.1109/iccad69956.2026.11643141

From Semantic Models to Sustainability Intelligence: A Review of Knowledge-Driven Reasoning in Smart Building Digital Twins

Alkhorasani, H., & Alawag, A. M. (2026) · International Conference on Emerging Smart Technologies and Applications (eSmarTA) · doi:10.1109/esmarta70636.2026.11652214

The Algorithm's Boundary: When AI enhances and fails to strengthen pharmaceutical supply chain resilience

Song, F., Li, S., & Jian, P. (2026) · Social Science & Medicine · doi:10.1016/j.socscimed.2026.119771

AI and Digital Twins: A Mutualistic Symbiotic Relationship

Chen, C., & Girolami, M. (2026) · Digital twins and applications. · doi:10.1049/dgt2.70051

Closed loop precision fermentation of circular bio-substrates: Integrating multi-omics and machine learning-assisted volatilomics

Kapila, A., Sherin, Z., Bashir, O., Mondal, I. H., Pawase, P. A., Hamid, Vahidi, M., & Abdi, G. (2026) · Applied Food Research · doi:10.1016/j.afres.2026.102533

Guest Editorial: Digital Twinning: Integrating AI-ML and Big Data Analytics for Virtual Representation

Gao, Z., Paul, A., & Wang, X. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3104815 · Cited by 30

Integration of AI-ML with Intelligent Digital Twins: A Comprehensive Review

Kaur, R., Sundharam, R. D., Uppal, M., & Gupta, D. (2024) · International Conference on Control, Computing, Communication and Materials (ICCCCM) · doi:10.1109/iccccm61016.2024.11039995

Artificial Intelligence-Based Digital Twins: A Systematic Review Integrated with Machine Learning and Deep Learning

Elwahsh, M., Hussein, M., El-Shafeiy, E., & Elhady, G. F. (2026) · IJCI International Journal of Computers and Information · doi:10.21608/ijci.2026.419574.1213

Role of artificial intelligence, machine learning, and the Internet of Things in designing digital twins

Kumar, A., Chawla, S. K., Ahlawat, D., & Soni, P. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-27388-9.00002-7

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