As the complexity of business and scenarios contin-ues to grow, the traditional, inefficient, and cumbersome domain modeling process can no longer adapt to the rapid iteration requirements. In recent years, technological breakthroughs in generative artificial intelligence (Generative AI), particularly in large language models (LLMs), present novel opportunities to enhance domain modeling efficiency. While LLMs demonstrate baseline capabilities in information extraction, their potential for constructing complex domain-specific models remains underexplored. This study investigates how LLMs can facilitate automated domain modeling tasks and support domain modeling education. Through systematic experimentation, we evaluate the impacts of LLM fine-tuning techniques and prompt engineering strategies on model outputs, while comparing two distinct generation modes: indirect model construction via domain element extraction versus direct domain model generation. Our empirical results demonstrate that LLMs exhibit significant potential in supporting high-efficiency domain modeling, with fine-tuning techniques and indirect generation modes yielding superior out-comes. Furthermore, we illustrate LLMs' utility as pedagogical tools for domain modeling education while identifying critical limitations and implementation risks that warrant consideration in both practical applications and future research.

Uncertainty exists widely in various fields, especially in industrial manufacturing. From traditional manufacturing to intelligent manufacturing, uncertainty always exists in the manufacturing process. With the integration of rapidly developing intelligent technology, the complexity of manufacturing scenarios is increasing, and the postdecision method cannot fully meet the needs of the high reliability of the process. It is necessary to research the pre‐elimination of uncertainty to ensure the reliability of process execution. Here, we analyze the sources and characteristics of uncertainty in manufacturing scenarios and propose a meta‐model architecture and uncertainty quantification (UQ) framework for uncertainty modeling. On the one hand, our approach involves the creation of a meta‐model structure that incorporates various strategies for uncertainty elimination (UE). On the other hand, we develop a comprehensive UQ framework that utilizes quantified metrics and outcomes to bolster the UE process. Finally, a deterministic model is constructed to guide and drive the process execution, which can achieve the purpose of controlling the uncertainty in advance and ensuring the reliability of the process. In addition, two typical manufacturing process scenarios are modeled, and quantitative experiments are conducted on a simulated production line and open‐source data sets, respectively, to illustrate the idea and feasibility of the proposed approach. The proposed UE approach, which innovatively combines the domain modeling from the software engineering field and the probability‐based UQ method, can be used as a general tool to guide the reliable execution of the process.