English(EN) FG-ConvNO: A Geometry-Aware Neural Operator for Propeller CFD Prediction

新的神经算子FG-ConvNO用有限数据预测螺旋桨CFD

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员开发了FG-ConvNO，这是一种新颖的神经算子，专为预测复杂几何形状中的计算流体动力学（CFD）而设计，特别适用于螺旋桨设计。该模型通过整合几何感知编码和离散连续卷积来实现离散化无关性，解决了训练数据有限的挑战。实验表明，FG-ConvNO在预测表面压力和整体推力方面优于现有方法，在低数据场景下表现出更强的稳定性和泛化能力。 AI

影响该模型可以显著降低工程设计模拟所需的计算成本和时间，从而加速开发周期。

排序理由该集群描述了一篇关于用于特定科学建模任务的新型神经算子的新学术论文。[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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Newsworthiness bucket: Tool; 该集群描述了一篇关于用于特定科学建模任务的新型神经算子的新学术论文。[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) · Yichen Di, Valentin Duruisseaux, Di Zhou, Xinyi Li, Daniel Leibovici, Jean Kossaifi, Anima Anandkumar · 2026-10-28 04:00

FG-ConvNO：一种几何感知神经算子用于螺旋桨CFD预测

arXiv:2610.33456v1 Announce Type: new Abstract: Computational Fluid Dynamics (CFD) is essential in contemporary engineering design, yet it remains expensive to compute and can take thousands of CPU/GPU hours or more for a single high-fidelity simulation. Recent developments in ma…

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报道来源 [1]

FG-ConvNO：一种几何感知神经算子用于螺旋桨CFD预测

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