Active operator learning with predictive uncertainty quantification for partial differential equations

doi:10.1016/j.jcp.2026.114791

2026-02-21

PRE

DOI：10.1016/j.jcp.2026.114791

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摘要

En 中文

• 深度神经网络算子配备了预测不确定性以准确估计误差。 • 不确定性预测泛化到分布外数据及函数空间。 • 我们引入了一种优化的推理实现，适用于实时应用。 • 预测不确定性用于加速函数空间中的主动学习。

Keyword:

Deep neural operators

Predictive uncertainty

Active learning

Partial differential equations

Uncertainty quantification

期刊

Journal of Computational Physics

IF:

3.8

论文数:

1.5W

被引数:

7.4W

机构

purdue university

学者数:

1.5K

论文数: 749

被引数: 1

yale university

学者数:

8.2K

论文数: 3.5K

被引数: 2

Sandia National Laboratories

学者数:

5.5K

论文数: 3.8K

被引数: 6.4K

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