Model updating is used to reduce error between measured structural responses and corresponding finite element (FE) model outputs, which allows accurate prediction of structural behavior in future analyses. In this work, reduced‐order parametrizations of an underlying FE model are developed from singular value decomposition (SVD) of the sensitivity matrix, thereby improving efficiency and posedness in model updating. A deterministic error minimization scheme is combined with asymptotic Bayesian inference to provide optimal regularization with estimates for model evidence and parameter efficiency. Natural frequencies and mode shapes are targeted for updating in a small‐scale example with simulated data and a full‐scale example with real data. In both cases, SVD‐based parametrization is shown to have good or better results than subset selection with very strong results on the full‐scale model, as assessed by Bayes factor.

中文翻译：

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有限元模型更新中基于灵敏度的奇异值分解参数化和最优正则化

模型更新用于减少测量的结构响应和相应的有限元（FE）模型输出之间的误差，从而可以在将来的分析中准确预测结构行为。在这项工作中，从灵敏度矩阵的奇异值分解（SVD）开发了基础有限元模型的降序参数化，从而提高了模型更新的效率和适用性。确定性误差最小化方案与渐近贝叶斯推理相结合，以提供最优正则化，并提供模型证据和参数效率的估计值。固有频率和模式形状的目标是在小规模示例中使用模拟数据进行更新，而在全尺寸示例中使用实际数据进行更新。在这两种情况下