English(EN) Learnable Splines for Unstructured Neural Operators

新型神经算子 L-SplineFNO 解决了非结构化网格上的偏微分方程求解问题

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-19 04:00

研究人员开发了 L-SplineFNO，这是一种新颖的神经算子，旨在高效地逼近非结构化网格上偏微分方程的解。这种新架构将傅里叶神经算子骨干与可学习的张量积 B 样条相结合，使其能够处理工程模拟中常见的复杂、不规则网格。与现有方法相比，L-SplineFNO 展现出强大的预测精度，同时显著降低了内存和计算需求，能够扩展到单 GPU 上的数百万个节点，并提供零样本超分辨率和严格的预测界限等功能。 AI

影响这项研究可能为复杂工程问题提供更高效、更准确的 AI 驱动的模拟。

排序理由详细介绍神经算子新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Vignesh Gopakumar, Zhuoyuan Wang, Ander Gray, Valentin Duruisseaux, Josh Williams, William Michael Edward Ellis, Nuno Nobre, Matthew Field, James Panayis, Alexander I. Blair, Andrew Davis, Stanislas Pamela, Robert Akers, Marc Peter Deisenroth, Anima Anan… · 2026-10-19 04:00

可学习样条用于非结构化神经算子

arXiv:2610.22446v1 Announce Type: new Abstract: Neural operators approximate solution operators of partial differential equations at a fraction of the cost of numerical solvers. Efficient grid-based architectures such as the Fourier neural operator (FNO) assume uniform rectangula…

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报道来源 [1]

可学习样条用于非结构化神经算子

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