Model-Based Systems Engineering (MBSE)
Model-Based Systems Engineering (MBSE) is a formalized modeling approach to systems engineering, representing a paradigm shift from document-centric to model-centric engineering. In traditional engineering, a document is a static, standalone artifact requiring manual updates, while a model is a dynamic, interconnected representation built on structured data that maintains relationships between system elements automatically[^c5]. The [[mbse/overview|MBSE Overview]] page covers its definition, history, methodology, maturity, and supporting tools and languages in depth.
The release of SysML v2 in September 2025 and the launch of its certification program in June 2026 marked the most significant evolution of systems modeling in nearly twenty years, with the language redesigned from the ground up to address long-standing limitations in precision, expressiveness, and tool interoperability[^c6]. The new language introduces a standard REST API, formal semantics built on the KerML foundation, and dual textual and graphical notation, enabling tool interoperability and digital thread integration across engineering ecosystems[^c10]. The US Department of Defense reinforced the adoption of model-based approaches by mandating digital engineering across all defense programs through DoDI 5000.97 in December 2023, establishing digital engineering ecosystems as the infrastructure necessary to support model-based approaches throughout system lifecycles[^c7].
The practice has been further advanced by the emergence of data-driven MBSE frameworks that integrate machine learning and AI as first-class architectural elements[^c9]. AI-augmented MBSE has gained significant momentum, with applications spanning requirements engineering — where NLP and ontologies transform unstructured documentation into traceable engineering artifacts[^c12] — neuro-symbolic formal verification for safety-critical systems, and conversational AI agents that enable natural-language querying of complex system models. Automation pipelines driven by REST APIs have emerged as a critical enabler for digital engineering workflows, though the lack of robust network-based APIs in many engineering tools remains a barrier to integration[^c11].
Research has demonstrated that MBSE provides greater accuracy and consistency across the entire system lifecycle by representing a unified and shared understanding of the system under consideration[^c8]. The history and maturity of MBSE from Wymore's 1993 book through the INCOSE SE Vision 2035 are covered in depth on the [[mbse/overview|MBSE Overview]] page. MBSE adoption faces challenges spanning technical, cultural, organizational, and educational dimensions, including tool interoperability issues, workforce competency gaps, and organizational resistance to change. Adoption spans aerospace, defense, automotive, rail, manufacturing, and other sectors, with organizations reporting quantified benefits including reduced development cycle lengths, earlier detection of design flaws, and improved cross-disciplinary collaboration.