## 1 Introduction
Systems engineering is in the process of switching between collaboration based on the use of data (authoritative sources of facts) and the use of models (collaborative integrated modeling environments) [19]. As shown in Figure 1, the digital engineering research roadmap developed by the SERC consists of five main goals: (1) model use for decision making; (2) authoritative source of truth (AST); (3) technological innovation; (4) collaborative environment; and (5) digital engineering workforce and cultural evolution. The development of digital engineering begins with data integration in AST, followed by semantic model integration, with the expectation of enabling augmented engineering to bring automation to engineering processes, system quality, and certification through the use of models and big data. Augmented engineering is at the core of AI4 SE. The yellow circles in the figure below indicate ongoing development work and the blue circles indicate work in transition areas. Utilizing augmented intelligence in systems engineering and engineering systems is known as augmentation engineering [20]. In this context, [PERSON] et al. [16] defined AI4 SE and SE4 AI. AI4 SE can be understood as the application of enhanced artificial intelligence and machine learning techniques to systems engineering practices aimed at large-scale model building and spatial design efforts. SE4 AI, on the other hand, refers to the application of systems engineering methodologies to the design and operation of learning-based systems, and its core research focus includes the development of learning-based system design principles, lifecycle evolution models, and model management strategies. In addition, [PERSON] et al. [21] have outlined the current areas of research and development in the engineering disciplines that are evolving in the course of digital transformation. [PERSON] [19], on the other hand, further summarizes the developments in the field of digital engineering that are closely related to AI4 SE, including the following:  
1. The use of classification combined with AI techniques for the underlying data. Interoperability between engineering data and program data based on domain-specific ontologies. 2. Authoritative and authentic data sources as the basis for techniques of program management activities in different engineering and complex engineering systems. In order to share data between programs and engineering designs, companies build large datasets to facilitate knowledge transfer and reuse between different programs and projects. 3. Based on knowledge representations such as ontologies, semantic rules provide a fundamental guarantee of integrity and consistency for AI-based reasoning engineering models. 4. Based on the real system model to realize automated digital twin, construct a fully dynamic copy of the virtual system and run in parallel with the physical system. Figure 1: SERC digital engineering roadmap [19]. System design and construction data will be updated from a unified data source corresponding to the real one. In MBSE modeling, domain knowledge serves as the foundation. This knowledge includes an understanding of the concepts, principles, rules, constraints and best practices of the domain where the system is located. Acquiring domain knowledge involves working with experts, reviewing literature and conducting field research. This deeper understanding helps modelers comprehend the system's nature and requirements, leading to more accurate and credible models. However, modeling requires systematic training and a significant investment of resources. Unfortunately, many domain experts lack the expertise required for modeling, even though they possess a deep understanding of their domain [22]. Providing system modelers with timely domain knowledge to support efficient modeling completion becomes a challenge.