Digital twins can be defined as digital representations of physical entities that employ real-time data to enable understanding of the operating conditions of these entities. Here we present a particular type of digital twin that involves a combination of computer vision, scientific machine learning, and augmented reality. This novel digital twin is able, therefore, to see, to interpret what it sees—and, if necessary, to correct the model it is equipped with—and presents the resulting information in the form of augmented reality. The computer vision capabilities allow the twin to receive data continuously. As any other digital twin, it is equipped with one or more models so as to assimilate data. However, if persistent deviations from the predicted values are found, the proposed methodology is able to correct on the fly the existing models, so as to accommodate them to the measured reality. Finally, the suggested methodology is completed with augmented reality capabilities so as to render a completely new type of digital twin. These concepts are tested against a proof-of-concept model consisting on a nonlinear, hyperelastic beam subjected to moving loads whose exact position is to be determined.

中文翻译：

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自我学习和自我纠正的数字双胞胎

数字孪生可以定义为物理实体的数字表示，它使用实时数据来了解这些实体的操作条件。在这里，我们展示了一种特殊类型的数字双胞胎，它结合了计算机视觉、科学机器学习和增强现实。因此，这种新颖的数字双胞胎能够看到、解释它所看到的——并在必要时纠正它配备的模型——并以增强现实的形式呈现结果信息。计算机视觉功能允许双胞胎连续接收数据。与任何其他数字双胞胎一样，它配备了一个或多个模型以吸收数据。但是，如果发现与预测值的持续偏差，所提出的方法能够即时纠正现有模型，以便使它们适应测量的现实。最后，建议的方法与增强现实功能一起完成，以呈现一种全新的数字双胞胎。这些概念是针对一个概念验证模型进行测试的，该模型由一个非线性、超弹性梁组成，该梁承受移动载荷，其确切位置有待确定。