English(EN) Multifidelity Operator Inference for Non-intrusive Model Reduction under Data Scarcity

新的多保真度算子推断方法提高了代理模型的准确性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 05:00

研究人员开发了一种名为多保真度算子推断（MF-OpInf）的新方法，以提高工程设计中使用的代理模型的准确性。该方法通过整合大量的低保真度数据来解决用有限的高保真度数据训练的模型中存在的高方差问题。MF-OpInf 使用多保真度线性回归框架来估计降阶算子，与传统算子推断方法相比，在计算预算受限的情况下，在偏微分方程问题上显示出更高的预测精度。 AI

影响提高了工程设计和不确定性量化模拟的效率和准确性。

排序理由该集群包含一篇详细介绍科学计算机器学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Vivian Zhang, Tomoki Koike, Atticus Rex, Elizabeth Qian · 2026-11-11 05:00

数据稀疏条件下多保真算子推理用于非侵入式模型降阶

arXiv:2611.13624v1 Announce Type: new Abstract: Many-query analyses, in which a computational model is repeatedly evaluated, play an important role in uncertainty quantification and optimization for engineering design. However, many-query analyses can be prohibitively expensive w…

本文如何被排名

报道来源 [1]

数据稀疏条件下多保真算子推理用于非侵入式模型降阶

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