English(EN) Uncertainty Quantification of Engineering Structures by Polynomial Chaos Expansion and Multivariate Active Learning

新的采样方法提高了工程领域AI代理模型的准确性

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-15 19:27

开发了一种新的自适应序列采样方法，用于构建多项式混沌展开代理模型，并将其推广到值域为向量的量。该方法旨在通过选择新的样本来平衡输入空间的探索和多输出方差信息的利用，从而提高代理模型的准确性和稳定性。与传统的拉丁超立方采样相比，该方法在工程问题上更有效，能更可靠地估计二阶统计量。 AI

影响这项研究可能带来更高效、更准确的AI驱动工程模拟，降低复杂模型的计算成本。

排序理由该集群包含一篇学术论文，详细介绍了一种使用AI技术对工程结构进行不确定性量化的新方法。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv stat.ML TIER_1 English(EN) · Qitian Lu, Jafar Jafari-Asl, Panagiotis Spyridis, Lukas Novak · 2026-06-17 04:00

Uncertainty Quantification of Engineering Structures by Polynomial Chaos Expansion and Multivariate Active Learning

arXiv:2606.17233v1 Announce Type: cross Abstract: In many engineering applications, a single high-fidelity model produces multiple quantities of interest (QoIs) under the same input parameters, e.g. finite element models of complex physical systems. To alleviate the high computat…
arXiv stat.ML TIER_1 English(EN) · Lukas Novak · 2026-06-15 19:27

Uncertainty Quantification of Engineering Structures by Polynomial Chaos Expansion and Multivariate Active Learning

In many engineering applications, a single high-fidelity model produces multiple quantities of interest (QoIs) under the same input parameters, e.g. finite element models of complex physical systems. To alleviate the high computational cost of direct model evaluations, surrogate …