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

arXiv: https://arxiv.org/abs/2506.06580 · HTML: https://mlsanity.com/paper/2506.06580/

## 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 (most cited first, 19 of 19)

- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey](https://mlsanity.com/paper/2009.13303/) (2020)
- [Sim-to-Real: Learning Agile Locomotion For Quadruped Robots](https://mlsanity.com/paper/1804.10332/) (2018)
- [Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience](https://mlsanity.com/paper/1810.05687/) (2018)
- [Meta-Sim: Learning to Generate Synthetic Datasets](https://mlsanity.com/paper/1904.11621/) (2019)
- [A Survey on Data Augmentation for Text Classification](https://mlsanity.com/paper/2107.03158/) (2021)
- [Sim2real transfer learning for 3D human pose estimation: motion to the rescue](https://mlsanity.com/paper/1907.02499/) (2019)
- [How Simulation Helps Autonomous Driving:A Survey of Sim2real, Digital Twins, and Parallel Intelligence](https://mlsanity.com/paper/2305.01263/) (2023)
- [Constructing Neural Network-Based Models for Simulating Dynamical Systems](https://mlsanity.com/paper/2111.01495/) (2021)
- [Increasing the Speed and Accuracy of Data LabelingThrough an AI Assisted Interface](https://mlsanity.com/paper/2104.04122/) (2021)
- [A Survey of Data Quality Measurement and Monitoring Tools](https://mlsanity.com/paper/1907.08138/) (2019)
- [Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin](https://mlsanity.com/paper/1907.01523/) (2019)
- [Assessing the Ability of ChatGPT to Screen Articles for Systematic Reviews](https://mlsanity.com/paper/2307.06464/) (2023)
- [Digital Twin Evolution for Sustainable Smart Ecosystems](https://mlsanity.com/paper/2403.07162/) (2024)
- [Digital Twin-Aided Learning for Managing Reconfigurable Intelligent Surface-Assisted, Uplink, User-Centric Cell-Free Systems](https://mlsanity.com/paper/2302.05073/) (2023)
- [Opinion-Guided Reinforcement Learning](https://mlsanity.com/paper/2405.17287/) (2024)
- [Guidelines for the Search Strategy to Update Systematic Literature Reviews in Software Engineering](https://mlsanity.com/paper/2006.05542/) (2020)
- [Current Trends in Digital Twin Development, Maintenance, and Operation: An Interview Study](https://mlsanity.com/paper/2306.10085/) (2023)
- [COOCK project Smart Port 2025 D3.1: "To Twin Or Not To Twin"](https://mlsanity.com/paper/2401.12747/) (2024)

## Built on by (most cited first, 6 of 6)

- [A Reference Architecture of Reinforcement Learning Frameworks](https://mlsanity.com/paper/2603.06413/) (2026)
- [Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents](https://mlsanity.com/paper/2510.17491/) (2025)
- [Developing AI Agents with Simulated Data: Why, what, and how?](https://mlsanity.com/paper/2602.15816/) (2026)
- [Artificial Intelligence for Modeling & Simulation in Digital Twins](https://mlsanity.com/paper/2602.19390/) (2026)
- [A Model-Driven Approach for Developing Families of Reinforcement Learning Environments](https://mlsanity.com/paper/2606.20324/) (2026)
- [Artificial Intelligence and Modeling & Simulation: An Overview](https://mlsanity.com/paper/2608.00366/) (2026)

## 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.