研究人员开发了一个新的贝叶斯反演框架，使用概率图模型（PGMs）来推断结构构件的健康状态。该方法解决了高维离散状态参数的似然函数构建和边际似然计算中的挑战。该框架利用图神经网络（GNNs）进行推断，并采用基于图属性的训练策略，以确保在不同图尺度上的准确性并降低计算成本。 AI
影响 引入了一种使用 GNNs 进行结构健康监测的新颖方法，有望提高基础设施的安全性和维护效率。
排序理由 这是一篇研究论文，详细介绍了使用 GNNs 和 PGMs 进行贝叶斯反演的新颖框架。
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arXiv:2604.23514v1 Announce Type: new Abstract: The health condition of components in civil infrastructures can be described by various discrete states according to their performance degradation. Inferring these states from measurable responses is typically an ill-posed inverse p…
The health condition of components in civil infrastructures can be described by various discrete states according to their performance degradation. Inferring these states from measurable responses is typically an ill-posed inverse problem. Although Bayesian methods are well-suite…
The health condition of components in civil infrastructures can be described by various discrete states according to their performance degradation. Inferring these states from measurable responses is typically an ill-posed inverse problem. Although Bayesian methods are well-suite…