Română(RO) Euclidean Fourier Neural Operators

欧几里得傅里叶神经网络算子提供独立于域的AI学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-31 04:00

研究人员推出了欧几里得傅里叶神经网络算子（EFNOs），作为对传统傅里叶神经网络算子（FNOs）的独立于域的改进。虽然FNOs受限于其对周期性域的依赖，但EFNOs将谱核参数化为物理波矢的连续函数。这使得EFNOs能够学习在不同形状和大小的周期性域上一致运行的算子。EFNO已在热方程和材料科学任务等问题上进行了测试，证明了其泛化到新网格大小和域的能力。 AI

影响 EFNOs有望在不同物理域的科学机器学习中实现更鲁棒的泛化。

排序理由介绍新模型架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 Română(RO) · Nathanael Bosch, Niklas Frederik Schmitz, Michael F. Herbst · 2026-08-31 04:00

欧几里得傅里叶神经算子

arXiv:2608.28425v1 Announce Type: new Abstract: Fourier neural operators (FNOs) provide an efficient framework for learning mappings between function spaces as they are, by construction, independent of the grid resolution at which they are trained and evaluated. However, FNOs are…

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欧几里得傅里叶神经算子

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