In engineering systems, a digital twin serves as a digital replica encompassing both physical assets and their associated processes, such as manufacturing and certification. The implementation of digital twins offers substantial potential for various applications, including improved design, enhanced collaboration, effective energy management, risk mitigation, lifecycle management, and predictive maintenance. However, existing definitions of a “twin” are often ambiguous and lack a structured approach for developing digital twins, particularly for systems with unknown inputs. This paper addresses these shortcomings by proposing a clear definition and a robust methodology for building digital twins. Our methodology integrates projection-based model order reduction, a rapid approach for identifying unknown inputs, and a non-parametric probabilistic method for modeling and quantifying model-form uncertainty. Additionally, it incorporates a probabilistic learning approach for performing stochastic model updating. The effectiveness of this digital twinning methodology is illustrated through a case study involving an elevated truss footbridge located at the Autodesk Research facility at Pier 9 in San Francisco with unknown inputs. This case study underscores the importance of accurately modeling uncertainty to enhance the performance and reliability of digital twins in real-world engineering applications.

中文翻译：

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从模型型 UQ 和数字孪生输入未知的系统的真实世界观察中进行概率学习


在工程系统中，数字孪生充当数字副本，包括物理资产及其相关流程，例如制造和认证。数字孪生的实施为各种应用提供了巨大的潜力，包括改进设计、增强协作、有效能源管理、风险缓解、生命周期管理和预测性维护。然而，“孪生”的现有定义往往模棱两可，并且缺乏开发数字孪生的结构化方法，特别是对于输入未知的系统。本文通过提出构建数字孪生的明确定义和强大的方法来解决这些缺点。我们的方法集成了基于投影的模型降阶、识别未知输入的快速方法，以及用于建模和量化模型形式不确定性的非参数概率方法。此外，它还结合了一种用于执行随机模型更新的概率学习方法。这种数字孪生方法的有效性通过一个案例研究来说明，该案例研究涉及位于旧金山 9 号码头的 Autodesk Research 设施的一座高架桁架人行天桥，其输入未知。本案例研究强调了准确建模不确定性对于提高数字孪生在实际工程应用中的性能和可靠性的重要性。