Digital Twin AI: Opportunities and Challenges from Large Language Models to World Models
🤖 AI Summary
This work addresses the challenges of scalability, interpretability, and trustworthiness encountered as digital twins evolve from passive simulation toward autonomous intelligence. It proposes the first AI-integrated framework spanning the entire digital twin lifecycle, structured into four stages: modeling, mirroring, intervention, and autonomous management. By synergistically integrating physics-informed learning with foundational models—including large language models, generative world models, and intelligent agents—the framework endows digital twins with reasoning, communication, and scenario-generation capabilities. Validated across eleven domains such as healthcare, aerospace, and smart manufacturing, the approach advances the cognitive and autonomous application of generative AI in digital twins and outlines key challenges and future development pathways.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Digital Twin
Artificial Intelligence
Large Language Models
World Models
Autonomous Systems
Innovation
Methods, ideas, or system contributions that make the work stand out.
Digital Twin
Large Language Models
Physics-Informed AI
Generative World Models
Autonomous Cognitive Systems
🔎 Similar Papers
No similar papers found.
