Digital Twin: A Comprehensive Study of ChatGPT 3.5

Digital twin facilitates real-life cases by using simulations. Using AI technologies such as ChatGPT 3.5 with user generation for complex research topics for decision-making cases gives a new model for digital win. This research paper presents a comprehensive study on digital twin modeling, leveraging the capabilities of ChatGPT 3.5 to automate the synthesis of diverse literature and generate valuable insights into this transformative technology compared to user analysis. The main objective of this study is to systematically explore the landscape of digital twin modeling, addressing its implications, challenges, and potential advancements and address the limitation of using ChatGPT 3.5 in analyzing limited case studies while the others need user experience. Different case studies related to decision making are discussed and the limitation of it is analyzed when ChatGPT 3.5 and user experience are included. In conclusion, ChatGPT 3.5 enhances data analysis capabilities, thereby contributing to the creation of a comprehensive study, but the human expert is still needed in decision-making cases.