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arXiv 2604.04321math.DGcs.LG
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通过神经流最小化Willmore能量

Minimising Willmore Energy via Neural Flow

Instituto de Matemática, Estatística e Computação Científica (IMECC), Universidade Estadual de Campinas (UNICAMP)(坎皮纳斯州立大学数学、统计与科学计算研究所)

Edward Hirst, Henrique N. Sá Earp, Tomás S. R. Silva

AI总结:

本文提出神经Willmore流作为最小化Willmore能量的自然演化过程,利用神经网络建模拓扑2D域到3D欧几里得空间的映射,并通过PINN式损失函数训练实现闭合曲面的最小能量嵌入。

摘要:

本文介绍了闭合定向$2$-曲面在$\mathbb{R}^3$中的神经Willmore流,作为最小化Willmore能量的自然演化过程,Willmore能量是平均曲率的平方$L^2$-范数。神经架构用于建模拓扑$2d$域到$3d$欧几里得空间的映射,其中学习过程最小化嵌入上Willmore能量的功能损失。训练再现了 genus $0$ 曲面的预期球面,以及 genus $1$ 曲面的克利福德 torus,分别。此外, genus $2$ 情况的实验提供了一种新的方法来寻找此开放问题中的最小Willmore曲面。

英文摘要:

The neural Willmore flow of a closed oriented $2$-surface in $\mathbb{R}^3$ is introduced as a natural evolution process to minimise the Willmore energy, which is the squared $L^2$-norm of mean curvature. Neural architectures are used to model maps from topological $2d$ domains to $3d$ Euclidean space, where the learning process minimises a PINN-style loss for the Willmore energy as a functional on the embedding. Training reproduces the expected round sphere for genus $0$ surfaces, and the Clifford torus for genus $1$ surfaces, respectively. Furthermore, the experiment in the genus $2$ case provides a novel approach to search for minimal Willmore surfaces in this open problem.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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