The computational cost associated with structural reliability analysis increases substantially when dealing with multiple response metrics and high-dimensional input spaces. To address this challenge, an innovative global prediction framework is proposed which leverages multi-output Gaussian process (MOGP) modeling. This framework reduces the computational burden for high-dimensional, multi-response systems by incorporating active subspace and adaptive sampling. The adaptive sampling technique strategically selects the most informative new data points for multi-response prediction by leveraging correlations between responses. Notably, the framework prevents premature termination in low-dimensional scenarios with unknown distributions. Additionally, a multi-response dependent active subspace dimension reduction method is employed to manage high-dimensional data, enhancing the stability of projected structural responses in the reduced-dimensional subspace. The effectiveness of the proposed framework is demonstrated through comprehensive case studies and comparative analyses with traditional approaches. The results demonstrate significant advantages in model dimension reduction, improved accuracy of structural prediction, and enhanced stability, making it well-suited for structural performance prediction.

中文翻译：

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一种基于自适应采样和主动子空间方法的可靠性工程中多响应模型的新型全局预测框架


在处理多响应度量和高维输入空间时，与结构可靠性分析相关的计算成本大幅增加。为了应对这一挑战，提出了一种利用多输出高斯过程 （MOGP） 建模的创新全局预测框架。该框架通过结合主动子空间和自适应采样来减轻高维、多响应系统的计算负担。自适应抽样技术通过利用响应之间的相关性，战略性地选择信息量最大的新数据点进行多响应预测。值得注意的是，该框架可以防止在具有未知分布的低维场景中过早终止。此外，采用多响应依赖的主动子空间降维方法来管理高维数据，增强了降维子空间中投影结构响应的稳定性。通过全面的案例研究和与传统方法的比较分析，证明了所提出的框架的有效性。结果显示，模型降维具有显著优势，提高了结构预测的准确性，增强了稳定性，使其非常适合结构性能预测。