Uncertainty Modeling for SysML v2

2026-02-25

AI Summary

This work addresses the lack of native support for uncertainty modeling in SysML v2, which hinders the explicit representation and reasoning about uncertainty in model-based systems engineering (MBSE). To bridge this gap, the study presents the first systematic integration of the OMG-standardized PSUM uncertainty metamodel into SysML v2. Through formal semantic extensions, the approach enables explicit modeling, structured characterization, and consistent propagation of uncertainty sources while preserving full compatibility with SysML v2’s syntax and semantics. By elevating uncertainty to a first-class modeling element, the proposed extension enhances MBSE’s capability to handle uncertainty-aware system design. The expressiveness and effectiveness of the approach are demonstrated across seven diverse case studies, confirming its utility in uncertainty-aware MBSE practices.

Technology Category

• Reasoning under Uncertainty: Uncertainty Representations
• Multiagent Systems: Multiagent Systems under Uncertainty
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Uncertainty is inherent in modern engineered systems, including cyber-physical systems, autonomous systems, and large-scale software-intensive infrastructures (such as microservice-based systems) operating in dynamic and partially observable environments. The recent publication of Precise Semantics for Uncertainty Modeling (PSUM) by the Object Management Group represents the first standardized specification for uncertainty modeling within the Model-Based Systems Engineering (MBSE) community, providing formally defined semantics for representing and reasoning about uncertainty in models. In parallel, the second version of Systems Modeling Language (SysML v2) was released as the next-generation systems modeling language, offering improved semantic rigor and reusability, yet lacking native constructs aligned with PSUM for first-class uncertainty representation. This paper proposes a systematic extension of SysML v2 that incorporates the PSUM metamodel into its modeling framework. The extension enables explicit specification of indeterminacy sources, structured characterization of uncertainties, and consistent propagation of uncertainty within system models, while preserving conformance with SysML v2 syntax and semantics. We validate the approach through seven case studies. Results demonstrate that the proposed extension (PSUM-SysMLv2) is expressive and applicable for uncertainty-aware MBSE, and potentially enables uncertainty and uncertainty propagation analyses.

Problem

• Uncertainty Modeling
• SysML v2
• Model-Based Systems Engineering
• PSUM
• Indeterminacy

Innovation

• Uncertainty Modeling
• SysML v2
• PSUM
• Model-Based Systems Engineering
• Metamodel Integration