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Structural model updating using adaptive multi-response Gaussian process meta-modeling

机译：结构模型使用自适应多响应高斯过程元建模更新

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摘要

Finite element model updating utilizing frequency response functions as inputs is an important procedure in structural analysis, design and control. This paper presents a highly efficient framework that is built upon Gaussian process emulation to inversely identify model parameters through sampling. In particular, a multi-response Gaussian process (MRGP) meta-modeling approach is formulated that can accurately construct the error response surface, i.e., the discrepancies between the frequency response predictions and actual measurement. In order to reduce the computational cost of repeated finite element simulations, an adaptive sampling strategy is established, where the search of unknown parameters is guided by the response surface features. Meanwhile, the information of previously sampled model parameters and the corresponding errors is utilized as additional training data to refine the MRGP meta-model. Two stochastic optimization techniques, i.e., particle swarm and simulated annealing, are employed to train the MRGP meta-model for comparison. Systematic case studies are conducted to examine the accuracy and robustness of the new framework of model updating.
机译：利用频率响应函数的有限元模型更新作为输入是结构分析，设计和控制的重要过程。本文介绍了一个高效的框架，基于高斯流程仿真，通过采样来反转模型参数。特别地，配制了多响应高斯过程（MRGP）元建模方法，其可以准确地构造误差响应表面，即频率响应预测和实际测量之间的差异。为了降低重复有限元模拟的计算成本，建立了自适应采样策略，其中通过响应表面特征搜索未知参数的搜索。同时，先前采样的模型参数的信息和相应的错误被用作额外的训练数据来改进MRGP元模型。使用两个随机优化技术，即粒子群和模拟退火，用于培训MRGP元模型进行比较。进行系统案例研究，以检查模型更新框架的准确性和稳健性。

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