English(EN) Joint Spatiotemporal Spectral Neural Operators for Learning PDEs on Irregular Domains

新的图谱神经网络算子在不规则域上学习偏微分方程

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了一种新的图谱神经网络算子（GSNO），旨在学习不规则域上偏微分方程（PDEs）的解。该方法结合了空间图谱分解和时间傅里叶变换，无需域变形或复杂的几何嵌入即可实现连贯的算子学习。GSNO在各种PDE基准测试中表现出高精度、缩短的运行时间和更少的参数数量，并显示出强大的泛化能力。 AI

影响这种新方法通过更好地学习复杂的物理系统，有望提高科学模拟的效率和准确性。

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arXiv cs.LG TIER_1 English(EN) · Abdolmehdi Behroozi, Chaopeng Shen · 2026-09-01 04:00

用于学习非结构化域上偏微分方程的联合时空谱神经网络算子

arXiv:2608.29892v1 Announce Type: new Abstract: Learning solution operators for partial differential equations (PDEs) on irregular and geometry-dependent domains remains a central challenge in scientific machine learning. While spectral methods provide strong inductive biases for…