Uncertainty Modeling for SysML v2

TL;DR

This paper introduces an extension to SysML v2 that integrates PSUM for explicit uncertainty modeling, enhancing the language's ability to represent and analyze uncertainties in complex engineered systems.

Contribution

It systematically incorporates PSUM into SysML v2, enabling explicit uncertainty specification and propagation while maintaining language conformance.

Findings

Extension is expressive and applicable for uncertainty-aware MBSE

Enables structured characterization and propagation of uncertainties

Validated through seven case studies

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.