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Digital Twin: Values, Challenges and Enablers

arXiv 1910.01719 ↗First posted 3 Oct 2019eess.SPcited by 81

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Abstract (arXiv)

A digital twin can be defined as an adaptive model of a complex physical system. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In this work, we review the recent status of methodologies and techniques related to the construction of digital twins. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.

Key facts. Cited by 81 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Oct 2019.

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Citing papers per year: 2021: 11; 2022: 20; 2023: 11; 2024: 13; 2025: 12; 2026: 14.

81 papers.

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  1. 2022A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning Enabling Technologiescited by 28
  2. 2021Deep neural network enabled corrective source term approach to hybrid analysis and modelingcited by 16
  3. 2021Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolutioncited by 14
  4. 2024Data-Driven Physics-Informed Neural Networks: A Digital Twin Perspectivecited by 12
  5. 2021Nonlinear proper orthogonal decomposition for convection-dominated flowscited by 11
  6. 2022MetaAID: A Flexible Framework for Developing Metaverse Applications via AI Technology and Human Editingcited by 11
  7. 2023A digital twin framework for civil engineering structurescited by 10
  8. 2021Model Order Reduction based on Runge-Kutta Neural Networkcited by 9
  9. 2022A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacousticcited by 7
  10. 2022Leveraging Industry 4.0 -- Deep Learning, Surrogate Model and Transfer Learning with Uncertainty Quantification Incorporated into Digital Twin for Nuclear Systemcited by 7
  11. 2023Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directionscited by 7
  12. 2022Combining physics-based and data-driven techniques for reliable hybrid analysis and modeling using the corrective source term approachcited by 6
38 more
  1. 2023Constrained optimization of sensor placement for nuclear digital twinscited by 6
  2. 2021Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemiccited by 5
  3. 2022textitFastSVD-ML-ROM: A Reduced-Order Modeling Framework based on Machine Learning for Real-Time Applicationscited by 5
  4. 2023Patient-specific, mechanistic models of tumor growth incorporating artificial intelligence and big datacited by 5
  5. 2022Policy Learning for Robust Markov Decision Process with a Mismatched Generative Modelcited by 4
  6. 2022Physics Guided Machine Learning for Variational Multiscale Reduced Order Modelingcited by 4
  7. 2022The Digital Twin Landscape at the Crossroads of Predictive Maintenance, Machine Learning and Physics Based Modelingcited by 4
  8. 2022Data-driven Science and Machine Learning Methods in Laser-Plasma Physicscited by 4
  9. 2022Artificial intelligence-driven digital twin of a modern house demonstrated in virtual realitycited by 4
  10. 2023Adaptive learning of effective dynamics: Adaptive real-time, online modeling for complex systemscited by 4
  11. 2022An AI-based Domain-Decomposition Non-Intrusive Reduced-Order Model for Extended Domains applied to Multiphase Flow in Pipescited by 3
  12. 2024Thermodynamics-informed super-resolution of scarce temporal dynamics datacited by 3
  13. 2021Detecting Label Noise via Leave-One-Out Cross-Validationcited by 2
  14. 2021Machine learning-based conditional mean filter: a generalization of the ensemble Kalman filter for nonlinear data assimilationcited by 2
  15. 2021Model Uncertainty and Correctability for Directed Graphical Modelscited by 2
  16. 2022Active-learning-based non-intrusive Model Order Reductioncited by 2
  17. 2022Decentralized digital twins of complex dynamical systemscited by 2
  18. 2022Physics-based Digital Twins for Autonomous Thermal Food Processing: Efficient, Non-intrusive Reduced-order Modelingcited by 2
  19. 2022Digital twins of nonlinear dynamical systemscited by 2
  20. 2024TwinLab: a framework for data-efficient training of non-intrusive reduced-order models for digital twinscited by 2
  21. 2024Digital Twin-based Driver Risk-Aware Intelligent Mobility Analytics for Urban Transportation Managementcited by 2
  22. 2024Predictive Digital Twin for Condition Monitoring Using Thermal Imagingcited by 2
  23. 2025Bridging the Reality Gap in Digital Twins with Context-Aware, Physics-Guided Deep Learningcited by 2
  24. 2025Digital Twin-Driven Pavement Health Monitoring and Maintenance Optimization Using Graph Neural Networkscited by 2
  25. 2021Fully differentiable model discoverycited by 1
  26. 2021Quantum Artificial Intelligence for the Science of Climate Changecited by 1
  27. 2022Learning Physics between Digital Twins with Low-Fidelity Models and Physics-Informed Gaussian Processescited by 1
  28. 2022Digital Twin Data Modelling by Randomized Orthogonal Decomposition and Deep Learningcited by 1
  29. 2023Physics-based Reduced Order Modeling for Uncertainty Quantification of Guided Wave Propagation using Bayesian Optimizationcited by 1
  30. 2023Digital twins of nonlinear dynamical systems: A perspectivecited by 1
  31. 2023Filtered Partial Differential Equations: a robust surrogate constraint in physics-informed deep learning frameworkcited by 1
  32. 2024Image-based Deep Learning for Smart Digital Twins: a Reviewcited by 1
  33. 2024Diagnostic Digital Twin for Anomaly Detection in Floating Offshore Wind Energycited by 1
  34. 2024Learning Paradigms and Modelling Methodologies for Digital Twins in Process Industrycited by 1
  35. 2024Photogrammetry for Digital Twinning Industry 4.0 (I4) Systemscited by 1
  36. 2024Physics Encoded Blocks in Residual Neural Network Architectures for Digital Twin Modelscited by 1
  37. 2024DDD-GenDT: Dynamic Data-driven Generative Digital Twin Frameworkcited by 1
  38. 2025Artificial Intelligence in Reactor Physics: Current Status and Future Prospectscited by 1

50 most cited of 81 All 81 (JSON)