Digital Twin AI: Opportunities and Challenges from Large Language Models to World Models
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.
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Rong Zhou
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2026-01-07T09:26:30.528Z
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Rong Zhou
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Dongping Chen
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Dongping Chen
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Zihan Jia
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Zihan Jia
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Yao Su
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Yixin Liu
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Yiwen Lu
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Yue Huang
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Yi Pan
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Xinliang Li
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Yohannes Abate
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Zhengzhong Tu
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2026-01-07T13:37:53.800Z
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Zhengzhong Tu
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Yu Yang
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Qingsong Wen
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Gengchen Mai
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Sunyang Fu
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Jiachen Li
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Ziran Wang
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Jing Huang
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Tianming Liu
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Yong Chen
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Lichao Sun
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Lifang He
Digital Twin AI: Opportunities and Challenges from Large Language Models to World Models
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.
AI integration in digital twins follows a four-stage framework encompassing modeling, mirroring, intervention, and autonomous management, leveraging physics-informed approaches, generative AI, and foundation models for intelligent system operation.
digital twins artificial intelligence physics-based modeling physics-informed AI real-time synchronization predictive modeling anomaly detection optimization strategies large language models foundation models generative AI generative world models intelligent agents digital twin lifecycle