Uncertainty is an inherent property of any complex system, especially those that integrate physical parts or operate in real environments. In this paper, we focus on the Digital Twins of adaptive systems, which are particularly complex to design, verify, and optimize. One of the problems of having two systems (the physical one and its digital replica) is that their behavior may not always be consistent. In addition, both twins are normally subject to different types of uncertainties, which complicates their comparison. In this paper we propose the explicit representation and treatment of the uncertainty of both twins, and show how this enables a more accurate comparison of their behaviors. Furthermore, this allows us to reduce the overall system uncertainty and improve its behavior by properly averaging the individual uncertainties of the two twins. An exemplary incubator system is used to illustrate and validate our proposal.

翻译：不确定性是任何复杂系统的固有属性，特别是那些集成物理部件或在真实环境中运行的系统。本文聚焦于自适应系统的数字孪生体，此类系统在设计、验证和优化方面尤为复杂。物理系统与其数字副本共存时面临的核心问题之一，是两者的行为可能无法始终保持一致。此外，两个孪生体通常受制于不同类型的不确定性，这进一步增加了比较的难度。本文提出显式表示并处理两个孪生体的不确定性，并展示该方法如何实现对其行为更精准的比较。进一步，通过恰当平均两个孪生体的个体不确定性，可降低系统整体不确定性并改善其行为。本文以示例孵化器系统验证并阐明所提方案。