数字孪生人工智能：从大语言模型到世界模型的机遇与挑战

Digital Twin AI: Opportunities and Challenges from Large Language Models to World Models

January 4, 2026

作者: Rong Zhou, Dongping Chen, Zihan Jia, Yao Su, Yixin Liu, Yiwen Lu, Dongwei Shi, Yue Huang, Tianyang Xu, Yi Pan, Xinliang Li, Yohannes Abate, Qingyu Chen, Zhengzhong Tu, Yu Yang, Yu Zhang, Qingsong Wen, Gengchen Mai, Sunyang Fu, Jiachen Li, Xuyu Wang, Ziran Wang, Jing Huang, Tianming Liu, Yong Chen, Lichao Sun, Lifang He

cs.AI

摘要

数字孪生作为物理系统的精确数字化表征，已通过人工智能技术的融合从被动仿真工具演变为智能自主实体。本文提出统一四阶段框架，系统化描述人工智能在数字孪生全生命周期中的融合路径，涵盖建模、镜像、干预与自主管理四大阶段。通过整合现有技术与实践，我们提炼出贯穿数字孪生生命周期的人工智能方法论体系：（1）基于物理机理与物理信息的人工智能方法构建实体孪生模型；（2）通过实时同步技术实现物理系统的数字化镜像；（3）借助预测建模、异常检测与优化策略对实体孪生进行干预；（4）利用大语言模型、基础模型与智能体实现自主管理。我们分析了物理建模与数据驱动学习的协同机制，重点阐释了物理系统建模从传统数值求解器向物理信息模型与基础模型的范式转变。进一步探讨生成式人工智能技术（包括大语言模型与生成式世界模型）如何将数字孪生转化为具备推理、交流与创造性场景生成能力的主动式自进化认知系统。通过对医疗保健、航空航天、智能制造、机器人技术、智慧城市等11个应用领域的跨域综述，我们识别出与可扩展性、可解释性及可信度相关的共性挑战，并为负责任的人工智能驱动数字孪生系统指明发展方向。

English

Digital twins, as precise digital representations of physical systems, have evolved from passive simulation tools into intelligent and autonomous entities through the integration of artificial intelligence technologies. This paper presents a unified four-stage framework that systematically characterizes AI integration across the digital twin lifecycle, spanning modeling, mirroring, intervention, and autonomous management. By synthesizing existing technologies and practices, we distill a unified four-stage framework that systematically characterizes how AI methodologies are embedded across the digital twin lifecycle: (1) modeling the physical twin through physics-based and physics-informed AI approaches, (2) mirroring the physical system into a digital twin with real-time synchronization, (3) intervening in the physical twin through predictive modeling, anomaly detection, and optimization strategies, and (4) achieving autonomous management through large language models, foundation models, and intelligent agents. We analyze the synergy between physics-based modeling and data-driven learning, highlighting the shift from traditional numerical solvers to physics-informed and foundation models for physical systems. Furthermore, we examine how generative AI technologies, including large language models and generative world models, transform digital twins into proactive and self-improving cognitive systems capable of reasoning, communication, and creative scenario generation. Through a cross-domain review spanning eleven application domains, including healthcare, aerospace, smart manufacturing, robotics, and smart cities, we identify common challenges related to scalability, explainability, and trustworthiness, and outline directions for responsible AI-driven digital twin systems.