论文标题
有效的替代替代的重要性抽样框架用于可靠性分析
An efficient surrogate-aided importance sampling framework for reliability analysis
论文作者
论文摘要
代替昂贵的评估性能功能可以大大加速可靠性分析。本文提出了一个新的两阶段框架，用于替代辅助的可靠性分析，名为“重要性抽样”（S4IS）。在第一阶段，建立了一个粗糙的替代物，以获取有关故障区域的信息。第二阶段放大到重要区域，并通过自适应选择其中的支持点来提高故障概率估计器的准确性。提出了学习功能来指导支持点的选择，以便可以动态平衡探索和剥削。作为一个通用框架，S4IS有可能合并不同类型的替代物（高斯过程，支持向量机，神经网络等）。 S4IS的有效性和效率通过五个说明性示例来验证，这些示例涉及系统可靠性，高度非线性极限状态功能，较小的故障概率和中等高维度。 S4IS的实现可在https://github.com/robinseaseaseaside/s4is上下载。
Surrogates in lieu of expensive-to-evaluate performance functions can accelerate the reliability analysis greatly. This paper proposes a new two-stage framework for surrogate-aided reliability analysis named Surrogates for Importance Sampling (S4IS). In the first stage, a coarse surrogate is built to gain the information about failure regions; the second stage zooms into the important regions and improves the accuracy of the failure probability estimator by adaptively selecting support points therein. The learning functions are proposed to guide the selection of support points such that the exploration and exploitation can be dynamically balanced. As a generic framework, S4IS has the potential to incorporate different types of surrogates (Gaussian Processes, Support Vector Machines, Neural Network, etc.). The effectiveness and efficiency of S4IS is validated by five illustrative examples, which involve system reliability, highly nonlinear limit-state function, small failure probability and moderately high dimensionality. The implementation of S4IS is made available to download at https://github.com/RobinSeaside/S4IS.
