mfEGRA：通过主动学习定位失效边界实现多保真高效全局可靠性分析
mfEGRA: Multifidelity Efficient Global Reliability Analysis through Active Learning for Failure Boundary Location
AI总结：
本文提出多保真主动学习方法 mfEGRA，通过多保真高斯过程代理模型和两阶段自适应采样准则定位失效边界，在多个测试问题上相比单保真 EGRA 显著降低可靠性分析计算成本。
摘要：
本文开发了 mfEGRA，一种多保真主动学习方法，使用数据驱动的自适应细化代理模型，用于可靠性分析中的失效边界定位。该工作针对使用蒙特卡洛采样对评估代价高昂的高保真模型进行可靠性分析时成本过高的问题，通过使用高保真模型的低成本近似模型来解决。该方法建立在高效全局可靠性分析（EGRA）方法的基础上，EGRA 是一种基于代理模型的方法，使用自适应采样来细化高斯过程代理模型，以利用单保真模型定位失效边界。我们的方法引入了一个两阶段自适应采样准则，该准则使用多保真高斯过程代理模型来利用具有不同保真度的多个信息源。该方法将 EGRA 的期望可行性准则与一步前瞻信息增益相结合，以围绕失效边界细化代理模型。mfEGRA 的计算节省取决于不同模型之间的差异，以及评估不同模型相对于高保真模型的相对成本。我们表明，与单保真 EGRA 相比，使用 mfEGRA 准确估计可靠性可在一个解析多模态测试问题上实现约 46% 的计算节省，在一个三维声学喇叭问题上实现 24% 的计算节省。我们还展示了在声学喇叭问题的实现中使用先验抽取的蒙特卡洛样本的效果，其中与单保真 EGRA 相比，mfEGRA 在三维情况下实现了 45% 的计算节省，在一个更罕见事件的四维情况下实现了 48% 的计算节省。
英文摘要：
This paper develops mfEGRA, a multifidelity active learning method using data-driven adaptively refined surrogates for failure boundary location in reliability analysis. This work addresses the issue of prohibitive cost of reliability analysis using Monte Carlo sampling for expensive-to-evaluate high-fidelity models by using cheaper-to-evaluate approximations of the high-fidelity model. The method builds on the Efficient Global Reliability Analysis (EGRA) method, which is a surrogate-based method that uses adaptive sampling for refining Gaussian process surrogates for failure boundary location using a single-fidelity model. Our method introduces a two-stage adaptive sampling criterion that uses a multifidelity Gaussian process surrogate to leverage multiple information sources with different fidelities. The method combines expected feasibility criterion from EGRA with one-step lookahead information gain to refine the surrogate around the failure boundary. The computational savings from mfEGRA depends on the discrepancy between the different models, and the relative cost of evaluating the different models as compared to the high-fidelity model. We show that accurate estimation of reliability using mfEGRA leads to computational savings of $\sim$46% for an analytic multimodal test problem and 24% for a three-dimensional acoustic horn problem, when compared to single-fidelity EGRA. We also show the effect of using a priori drawn Monte Carlo samples in the implementation for the acoustic horn problem, where mfEGRA leads to computational savings of 45% for the three-dimensional case and 48% for a rarer event four-dimensional case as compared to single-fidelity EGRA.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
