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

TL;DR

This paper introduces a comprehensive four-stage framework for integrating AI into digital twins, emphasizing the role of large language models and world models in enabling autonomous, intelligent, and proactive digital twin systems across various domains.

Contribution

It systematically characterizes AI integration in digital twins through modeling, mirroring, intervention, and autonomous management, highlighting the shift to foundation and generative AI technologies.

Findings

AI enhances digital twin capabilities in multiple domains.

Large language models enable autonomous decision-making.

Challenges include scalability, explainability, and trustworthiness.

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.