mlsanityData to 13 Oct 2026

A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning Enabling Technologies

arXiv 2208.14197 ↗First posted 26 Aug 2022cs.CE cs.AI cs.LGcited by 28

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Abstract (arXiv)

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision and policy making, and more, by comprehensively modeling the physical world as a group of interconnected digital models. In a two-part series of papers, we examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins. This first paper presents a thorough literature review of digital twin trends across many disciplines currently pursuing this area of research. Then, digital twin modeling and twinning enabling technologies are further analyzed by classifying them into two main categories: physical-to-virtual, and virtual-to-physical, based on the direction in which data flows. Finally, this paper provides perspectives on the trajectory of digital twin technology over the next decade, and introduces a few emerging areas of research which will likely be of great use in future digital twin research. In part two of this review, the role of uncertainty quantification and optimization are discussed, a battery digital twin is demonstrated, and more perspectives on the future of digital twin are shared.

Key facts. Cited by 28 papers in the map (data through 13 Oct 2026). Builds on 58 of its 441 references that are in the map. First posted on arXiv 26 Aug 2022.

Builds on

58 of 441 references.

References · 58

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38 more
  1. 2020Learning under Concept Drift: A Reviewcited by 474
  2. 2019Federated Machine Learning: Concept and Applicationscited by 411
  3. 2019Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Datacited by 262
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  12. 2019Digital Twin: Values, Challenges and Enablerscited by 81
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  18. 2020A Probabilistic Graphical Model Foundation for Enabling Predictive Digital Twins at Scalecited by 58
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  25. 2020Adaptive Federated Learning and Digital Twin for Industrial Internet of Thingscited by 27
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50 most cited of 58 All 58 (JSON)

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2022: 2; 2023: 9; 2024: 9; 2025: 7; 2026: 1.

28 papers.

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  1. 2023DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Trainingcited by 76
  2. 2023Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorialcited by 27
  3. 2022A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systemscited by 18
  4. 2022A Comprehensive Review of Digital Twin -- Part 2: Roles of Uncertainty Quantification and Optimization, a Battery Digital Twin, and Perspectivescited by 17
  5. 2023Calibrating Wireless Ray Tracing for Digital Twinning using Local Phase Error Estimatescited by 14
  6. 2023A digital twin framework for civil engineering structurescited by 10
  7. 2023A Review of Digital Twins and their Application in Cybersecurity based on Artificial Intelligencecited by 9
  8. 2024Nonlinear Assimilation via Score-based Sequential Langevin Samplingcited by 8
  9. 2023The stochastic digital human is now enrolling for in silico imaging trials -- Methods and tools for generating digital cohortscited by 6
  10. 2024Investigation on domain adaptation of additive manufacturing monitoring systems to enhance digital twin reusabilitycited by 2
  11. 2025PAINT: Parallel-in-time Neural Twins for Dynamical System Reconstructioncited by 2
  12. 2023Physics-based Reduced Order Modeling for Uncertainty Quantification of Guided Wave Propagation using Bayesian Optimizationcited by 1
16 more
  1. 2024Image-based Deep Learning for Smart Digital Twins: a Reviewcited by 1
  2. 2024End-to-End Mesh Optimization of a Hybrid Deep Learning Black-Box PDE Solvercited by 1
  3. 2024Physics Encoded Blocks in Residual Neural Network Architectures for Digital Twin Modelscited by 1
  4. 2025An Attention-based Spatio-Temporal Neural Operator for Evolving Physicscited by 1
  5. 2025Subspace-based Approximate Hessian Method for Zeroth-Order Optimizationcited by 1
  6. 2025A generative adversarial network optimization method for damage detection and digital twinning by deep AI fault learning: Z24 Bridge structural health monitoring benchmark validationcited by 1
  7. 2023Digital Twin Graph: Automated Domain-Agnostic Construction, Fusion, and Simulation of IoT-Enabled Worldcited by 0
  8. 2023Surrogate modeling for stochastic crack growth processes in structural health monitoring applicationscited by 0
  9. 2024Foundations for Digital Twinscited by 0
  10. 2024Augmenting train maintenance technicians with automated incident diagnostic suggestionscited by 0
  11. 2024A Review of Graph-Powered Data Quality Applications for IoT Monitoring Sensor Networkscited by 0
  12. 2024Off the Normal Path: Learning Spatial Density Models of Node Mobilitycited by 0
  13. 2025A Digital Twin Framework for Generation-IV Reactors with Reinforcement Learning-Enabled Health-Aware Supervisory Controlcited by 0
  14. 2025Digital Twin-based Control Co-Design of Full Vehicle Active Suspensions via Deep Reinforcement Learningcited by 0
  15. 2025Adaptive digital twins for predictive decision-making: Online Bayesian learning of transition dynamicscited by 0
  16. 2026A Continual Validation, Updating, and Decision-Making Framework for Self-Adaptive Digital Twins via Robust Model Predictive Control: A Case Study in Additive Manufacturingcited by 0