English(EN) Instrumental Transfer Learning for Solution Operators

新的迁移学习方法解决了微分方程稀疏数据难题

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

研究人员开发了一种新颖的工具变量迁移学习方法，以解决学习微分方程解算子时数据稀缺的挑战。该方法通过考虑控制方程中未知的稀疏差异，利用来自相关源系统的丰富数据。利用分辨率恒等式和白化工具变量技术，该方法构建了目标算子的无偏源估计方程。提出的三阶段估计器包括仅目标岭回归初步估计、逐源稀疏校准以及最小化校准误差的投影再拟合，并采用迭代再校准方案进一步提高精度。理论界限证明了这种迁移学习何时可以匹配所有源数据的汇集速率，而模拟以及电子带通滤波器的应用表明其比现有方法有显著改进。 AI

影响该方法可以提高在观测数据有限的复杂科学数据上训练AI模型的效率。

排序理由该集群包含一篇详细介绍新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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Signal score: 1 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇详细介绍新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Tongyu Li, Zhuoyuan Li, Ruoxu Tan · 2026-11-02 05:00

Instrumental Transfer Learning for Solution Operators

arXiv:2610.41262v1 Announce Type: cross Abstract: Data-driven learning for the solution operator of a differential equation has broad scientific importance, but observations from a target system are often scarce and costly to collect. Related source systems may provide abundant o…

本文如何被排名

报道来源 [1]

Instrumental Transfer Learning for Solution Operators

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