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arXiv 2407.08031math.DG

黎曼子流形的粗外曲率

Coarse extrinsic curvature of Riemannian submanifolds

Marc Arnaudon, Xue-Mei Li, Benedikt Petko

AI总结:

本文受Ollivier粗Ricci曲率启发,提出基于Wasserstein 1-距离的黎曼子流形粗外曲率概念,并给出从Poisson点云等统计数据近似平均曲率的方法,可应用于流形学习与度量嵌入中的几何信息推断。

摘要:

受Ollivier粗Ricci曲率概念的启发,我们为黎曼子流形引入了一种新的粗外曲率概念。该曲率由支撑在子流形管状邻域内的概率测度之间的Wasserstein 1-距离导出,为等距嵌入欧氏空间中的流形外曲率提供了新的见解。该框架还提供了一种从统计数据(例如由Poisson点过程生成的点云)中近似平均曲率的方法。该方法在流形学习和度量嵌入研究中具有潜在应用,能够从经验数据中推断几何信息。

英文摘要:

We introduce a novel concept of coarse extrinsic curvature for Riemannian submanifolds, inspired by Ollivier's notion of coarse Ricci curvature. This curvature is derived from the Wasserstein 1-distance between probability measures supported in the tubular neighborhood of a submanifold, providing new insights into the extrinsic curvature of isometrically embedded manifolds in Euclidean spaces. The framework also offers a method to approximate the mean curvature from statistical data, such as point clouds generated by a Poisson point process. This approach has potential applications in manifold learning and the study of metric embeddings, enabling the inference of geometric information from empirical data.

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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