Empowering Digital Twins with Generative AI: Applications, Case Studies, and Limitations

Abstract The rise of the Generative Pre-Trained Transformer(GPT) language model, more commonly used as ChatGPT, has brought a spotlight on the ever-developing field of Generative AI (GAI). With current advancements in graphics processing units (GPUs), training and using deep generative models have become easier. Similarly, the developments in edge computing have made it possible to make the most of GAI’s potential for numerous use cases. One such use case involves its integration with Digital Twin technology, offering new possibilities for enhanced simulations, predictive analytics, and real-time decision-making. This paper explores the prospects of combining GAI with Digital Twin technology to create innovative solutions for applications like healthcare, manufacturing, agriculture, cybersecurity, etc. It also discusses how GAI can train Digital Twins by simulating various scenarios and providing different data sets, enabling predictive maintenance. It also delves into the real-world scenarios in which the technology has been implemented. The paper concludes with the limitations of GAI technology for Digital Twin applications, which can be worked upon in the future.