研究人员引入了各向同性傅里叶神经网络算子，这是对现有傅里叶神经网络算子的改进，旨在更好地尊重许多物理系统中固有的对称性。这种新方法通过在2D中最多减少16倍，在3D中最多减少96倍的参数量，从而提高了模型性能并显著减少了所需参数的数量。这些算子能够学习和求解偏微分方程，其速度通常超过传统方法。 AI
影响 为物理信息深度学习模型引入了一种参数效率更高、可能更准确的方法。
排序理由 该集群包含一篇arXiv预印本，详细介绍了一种新的深度学习模型方法。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2605.02597v1 Announce Type: new Abstract: Fourier Neural Operators are deep learning models that learn mappings between function spaces and can be used to learn and solve partial differential equations (PDEs), in some cases significantly faster than traditional PDE solvers.…
Fourier Neural Operators are deep learning models that learn mappings between function spaces and can be used to learn and solve partial differential equations (PDEs), in some cases significantly faster than traditional PDE solvers. Within the model are Fourier layers, which appl…