## 1 Introduction  
The evolution of complex systems in technology and ecology is undergoing profound changes due to the overturning of technological waves and the reshaping of industrial structure [1]. The complexity of these complex engineered systems, as well as the high demands on their performance, are constantly climbing. In recent decades, driven by innovative products in various industries, system development has encountered multiple challenges such as extreme system complexity, short lifecycles and long development cycles [2]. The traditional development model, which overly relies on documentation as the main channel of information transfer and physical testing as the main means of validation, has inherent risks and shortcomings that are gradually surfacing. Guided by the urgent needs of industrial research and the theory of systems engineering [3], the exploration and implementation of model-based systems engineering (MBSE) [4] in the conceptual design phase of a project gradually took off. Nowadays, the specification and integration of digital technologies for MBSE have gained widespread attention and application in the whole-life-cycle R&D process [5, 6], thus promoting the realization of innovative development paths,such as document-centered modeling methodology, whole life-cycle extended application and comprehensive simulation across disciplines. MBSE modeling is an approach that emphasizes the use of models to describe, analyze, design and verify a system by incorporating domain-specific expertise during the system development process. The MBSE methodology [7; 8; 9; 10] provides a systematic set of tools and techniques to enable the process from requirement capture to system design, and verification throughout the development process. The modeling team can translate domain knowledge into executable models to identify potential problems and risks early on and make timely adjustments and optimizations. MBSE is a widely used systems engineering approach. However, it still has many disadvantages and significant challenges that are difficult to overcome [11; 12]. Many enterprises still adhere to traditional systems engineering design and development methods. The adoption of MBSE in the enterprise is also limited by the availability of skilled practitioners [13; 14; 15]. Additionally, their informationization ability is insufficient, leading to the formation of a'model silo'. The integration of artificial intelligence (AI) with systems engineering is being considered as a means to enhance the level of intelligence in design, optimize decision making, and improve system efficiency and reliability in the face of complexity and uncertainty [16]. In 2019, the Research Committee of the Systems Engineering Research Center (SERC) at the University Affiliated Research Center (UARC), funded by the U.S. Department of Defense, designed a blueprint to guide and lead the direction of AI and autonomy research. The blueprint emphasizes the need to drive novel systems engineering practices to support the development of a new generation of automated, adaptive, and learning systems, collectively referred to as the 'SE of AI'. In the same year, at the Future of Systems Engineering (FuSE) symposium organized by the International Council on Systems Engineering (INCOSE), [PERSON] et al. [17] introduced the concept of 'AI for SE'. This two-way shift was characterized as 'AI for SE' and 'SE for AI'. The SERC Research Committee has created a blueprint to guide research on AI and autonomy. The purpose of this blueprint is to deepen our understanding and focus on the development process. Its aim is to establish connections between the systems engineering discipline and various trends in the field of AI, and to explore the application of these trends to systems automation. The blueprint was elaborated by [PERSON], [PERSON] et al. [18] and has been presented and discussed in various forums. Digital engineering forms the basis for all three cross-cutting tasks of SERC and the resulting research roadmap.