English(EN) F$^3$NO: Frequency-Decomposed Finite-Time Flow-map Neural Operators with Cross-Scale Conditioning

新的 F$^3$NO 神经网络算子提高了 PDE 预测精度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

研究人员开发了一种名为 F$^3$NO 的新型神经网络算子模型，旨在提高偏微分方程 (PDE) 预测的准确性和分辨率。该模型分解频率信息，使低频特征能够指导每一层内高频细节的细化。F$^3$NO 直接预测未来状态，并可以将并行预测与递归传播相结合以获得更长的轨迹，在五个 PDE 基准测试中显示出比现有方法更高的准确性。 AI

影响引入了一种新颖的神经网络算子架构，可以提高科学模拟的准确性和效率。

排序理由详细介绍用于科学预测的新模型的学术论文。 [lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Fan Wu, Cheng Jing, Kookjin Lee · 2026-10-09 04:00

F$^3$NO：具有跨尺度条件的频率分解有限时间流图神经网络算子

arXiv:2610.10998v1 Announce Type: new Abstract: Neural operators enable fast PDE forecasting, but repeated predictions accumulate errors and fine-scale structures remain difficult to resolve. We introduce a frequency-decomposed finite-time flow-map neural operator (F$^3$NO) that …

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F$^3$NO：具有跨尺度条件的频率分解有限时间流图神经网络算子

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