English(EN) AL-SPCE - Reliability analysis for nondeterministic models using stochastic polynomial chaos expansions and active learning

新的AL-SPCE方法增强了随机模型的可靠性分析

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

研究人员开发了一种名为AL-SPCE的新方法，该方法结合了主动学习和随机多项式混沌展开，以改进非确定性模型的可靠性分析。与传统的蒙特卡洛模拟和之前的基于代理的方法相比，这种方法显著降低了计算成本。AL-SPCE识别出模拟器具有高预测不确定性的区域，从而实现更有效和准确的可靠性估计，这一点已在三种问题类型中得到验证。 AI

影响该方法可能导致对复杂系统中可靠性评估的提高，而传统方法在此类系统中计算成本过高。

排序理由该集群包含一篇研究论文，详细介绍了非确定性模型可靠性分析的新方法。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv stat.ML 阅读 →

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报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · A. Pires, M. Moustapha, S. Marelli, B. Sudret · 2026-09-02 04:00

AL-SPCE - 使用随机多项式混沌展开和主动学习对非确定性模型进行可靠性分析

arXiv:2507.04553v2 Announce Type: replace-cross Abstract: Reliability analysis traditionally relies on deterministic simulators, where identical inputs yield identical outputs. However, many real-world systems exhibit stochastic behavior, producing non-repeatable outcomes even un…

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报道来源 [1]

AL-SPCE - 使用随机多项式混沌展开和主动学习对非确定性模型进行可靠性分析

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