用数字孪生构建AI仿真环境，解决数据不足难题
AI Simulation by Digital Twins: Systematic Survey, Reference Framework, and Mapping to a Standardized Architecture
- 通过数字孪生构建高保真虚拟训练环境
- 基于22项研究提炼出可落地的参考框架
- 适配国际标准架构，指导未来AI仿真设计
现代子符号人工智能面临数据量不足与质量不佳的挑战。为缓解这一问题，AI仿真利用虚拟训练环境，让智能体在合成数据中安全高效地开发。数字孪生作为物理系统的高保真虚拟副本，集成先进模拟器并可与真实系统交互以补充数据，为AI仿真开辟新路径。本文通过对22项核心研究进行系统性调研，识别技术趋势，构建参考框架以定位数字孪生与AI组件的关系。基于此，将框架映射至ISO 23247数字孪生参考架构，提出架构指南，并指出未来研究挑战与机遇。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。