mlsanityData to 14 Oct 2026

AI Simulation by Digital Twins: Systematic Survey, Reference Framework, and Mapping to a Standardized Architecture

arXiv 2506.06580 ↗First posted 6 Jun 2025cs.AI cs.ET cs.SEcited by 6

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Abstract (arXiv)

Insufficient data volume and quality are particularly pressing challenges in the adoption of modern subsymbolic AI. To alleviate these challenges, AI simulation uses virtual training environments in which AI agents can be safely and efficiently developed with simulated, synthetic data. Digital twins open new avenues in AI simulation, as these high-fidelity virtual replicas of physical systems are equipped with state-of-the-art simulators and the ability to further interact with the physical system for additional data collection. In this article, we report on our systematic survey of digital twin-enabled AI simulation. By analyzing 22 primary studies, we identify technological trends and derive a reference framework to situate digital twins and AI components. Based on our findings, we derive a reference framework and provide architectural guidelines by mapping it onto the ISO 23247 reference architecture for digital twins. Finally, we identify challenges and research opportunities for prospective researchers.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 19 of its 114 references that are in the map. First posted on arXiv 6 Jun 2025.

Builds on

19 of 114 references.

References · 19

  1. 2017Generative Adversarial Networks: An Overviewcited by 590
  2. 2020Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Surveycited by 413
  3. 2018Sim-to-Real: Learning Agile Locomotion For Quadruped Robotscited by 319
  4. 2018Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experiencecited by 217
  5. 2019Meta-Sim: Learning to Generate Synthetic Datasetscited by 128
  6. 2021A Survey on Data Augmentation for Text Classificationcited by 111
  7. 2019Sim2real transfer learning for 3D human pose estimation: motion to the rescuecited by 76
  8. 2023How Simulation Helps Autonomous Driving:A Survey of Sim2real, Digital Twins, and Parallel Intelligencecited by 58
  9. 2021Constructing Neural Network-Based Models for Simulating Dynamical Systemscited by 31
  10. 2021Increasing the Speed and Accuracy of Data LabelingThrough an AI Assisted Interfacecited by 21
  11. 2019A Survey of Data Quality Measurement and Monitoring Toolscited by 19
  12. 2019Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twincited by 17
7 more
  1. 2023Assessing the Ability of ChatGPT to Screen Articles for Systematic Reviewscited by 10
  2. 2024Digital Twin Evolution for Sustainable Smart Ecosystemscited by 4
  3. 2023Digital Twin-Aided Learning for Managing Reconfigurable Intelligent Surface-Assisted, Uplink, User-Centric Cell-Free Systemscited by 3
  4. 2024Opinion-Guided Reinforcement Learningcited by 3
  5. 2020Guidelines for the Search Strategy to Update Systematic Literature Reviews in Software Engineeringcited by 2
  6. 2023Current Trends in Digital Twin Development, Maintenance, and Operation: An Interview Studycited by 1
  7. 2024COOCK project Smart Port 2025 D3.1: "To Twin Or Not To Twin"cited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 5.

6 papers.

  1. 2026A Reference Architecture of Reinforcement Learning Frameworkscited by 3
  2. 2025Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agentscited by 2
  3. 2026Developing AI Agents with Simulated Data: Why, what, and how?cited by 0
  4. 2026Artificial Intelligence for Modeling & Simulation in Digital Twinscited by 0
  5. 2026A Model-Driven Approach for Developing Families of Reinforcement Learning Environmentscited by 0
  6. 2026Artificial Intelligence and Modeling & Simulation: An Overviewcited by 0