SuSIE：具有无畏探索的子集模拟
SuSIE: Subset Simulation with Intrepid Exploration
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中文总结 AI 辅助
研究子集模拟框架在多失效区域等复杂情况下估计结构可靠性的挑战，提出用改进的无畏MCMC采样器替代传统方法，经示例验证该方法能解决复杂特征，有效估计失效概率。
中文摘要 AI 辅助
这项工作探讨了子集模拟框架在涉及多个可能不相连的失效区域、或不连续或急剧变化的性能函数的设置中与估计结构可靠性相关的挑战。我们证明，子集模拟的这些缺点源于该方法中使用的马尔可夫链蒙特卡罗（MCMC）采样器的局限性，而非子集模拟框架本身。传统的随机游走Metropolis算法在从多模态分布采样时存在严重困难，通常用于子集模拟，导致在这种情况下失效概率估计不准确。在这项工作中，我们使用了改进版的无畏MCMC采样器，最近已证明它在从多模态概率分布采样时比普通随机游走Metropolis算法更有效。使用所提出的无畏采样器的子集模拟方法被证明可以解决这些复杂特征。考虑了几个说明性示例，维度从2到1003，展示了多个失效区域或高度非线性性能函数，包括解析问题和结构工程应用。
英文摘要
This work explores the challenges associated with the subset simulation framework - a well-established algorithm for estimating structural reliability - in settings involving multiple, possibly disconnected regions of failure, or involving discontinuous or sharply changing performance functions. We demonstrate that these drawbacks of subset simulation stem from the limitations of the Markov chain Monte Carlo (MCMC) sampler employed within the method, not from the subset simulation framework itself. Traditional random-walk Metropolis algorithms, which are known to struggle severely with sampling from multimodal distributions, are conventionally applied within subset simulation, which leads to inaccurate failure probability estimates in such cases. In this work, we instead utilize a modified version of the Intrepid MCMC sampler, which has recently been shown to be more effective than vanilla random-walk Metropolis algorithms in sampling from multimodal probability distributions. The subset simulation method with the proposed Intrepid sampler is demonstrated to address these complicating features. Several illustrative examples are considered, ranging from 2 to 1003 dimensions and exhibiting multiple failure regions or highly nonlinear performance functions, including both analytical problems and structural engineering applications.
