论文标题
基于自适应抽样的神经网络框架，用于可变刚度复合层压板的敏感性预测与混合不确定性
Adaptive Importance Sampling based Neural Network framework for Reliability and Sensitivity Prediction for Variable Stiffness Composite Laminates with hybrid uncertainties
论文作者
论文摘要
在这项工作中，我们建议利用基于人工神经网络（ANN）的二阶可靠性方法（SORM）和重要性抽样的优势，以产生基于自适应的重要性采样的ANN，并在多个独立的无独立图中，对可变刚度复合层压板（VSCL）的可变刚度复合层压板（VSCL）的敏感性和敏感性估计的具体应用。案例研究的性能函数是根据VSCL板的基本频率定义的。发现使用该方法的可靠性估计和灵敏度研究的准确性与使用基于ANN的Brute-Force MCS方法获得的敏感性研究密切一致，其计算节省为95 \％。此外，在敏感性研究部分中，考虑到层厚度的随机性对于故障概率估计的重要性也被强调。
In this work, we propose to leverage the advantages of both the Artificial Neural Network (ANN) based Second Order Reliability Method (SORM) and Importance sampling to yield an Adaptive Importance Sampling based ANN, with specific application towards failure probability and sensitivity estimates of Variable Stiffness Composite Laminate (VSCL) plates, in the presence of multiple independent geometric and material uncertainties. The performance function for the case studies is defined based on the fundamental frequency of the VSCL plate. The accuracy in both the reliability estimates and sensitivity studies using the proposed method were found to be in close agreement with that obtained using the ANN based brute-force MCS method, with a significant computational savings of 95\%. Moreover, the importance of taking into account the randomness in ply thickness for failure probability estimates is also highlighted quantitatively under the sensitivity studies section.
