清华主页 EN
导航菜单

Systolic inequality on Riemannian manifold with bounded Ricci curvature

来源: 02-28

时间:Tue.13:30-14:30pm, Feb. 28, 2023

地点:Jinchunyuan West Building, Conference Room 3 Zoom ID: 405 416 0815; PW: 111111

组织者:陈伟彦、高鸿灏、黄意、林剑锋、江怡

主讲人:Zhifei ZHU 朱知非(YMSC)

Abstract

In this talk, we show that the length of a shortest closed geodesic on a Riemannian manifold of dimension 4 with diameter D, volume v, and |Ric|<3 can be bounded by a function of v and D. In particular, this function can be explicitly computed if the manifold is Einstein. The proof of this result depends on a structural theorem proven by J. Cheeger and A. Naber. This is joint work with N. Wu.


Speaker

I am currently a postdoctoral fellow at Yau Mathematical Sciences Center. I am specializing in the field of Quantitative Geometry, which is a branch of Geometry that, in particular, studies the geometric inequalities and effective versions of topological existence theorems. I obtained my Ph.D. in 2019 from University of Toronto under the supervision of Prof. Regina Rotman.

个人主页:

https://zhifeizhu92.github.io/


返回顶部
相关文章
  • Systolic Inequality and Topological Complexity of Manifolds

    Abstract:The systole of a closed Riemannian manifold is defined to be the length of a shortest noncontractible loop. Gromov's systolic inequality relates systole to volume, which is a curvature free inequality. Gromov proved that systolic inequality holds on closed essential manifolds. Gromov's further work indicates that systolic inequality is related to topological complicatedness of manifol...

  • Sharp estimates of the heat kernel and Green’s function on the complete manifold with nonnegative Ricci curvature

    In this talk, we discuss the global behaviors of the heat kernel and Green's function one the complete manifold with nonnegative Ricci curvature. we first obtain sharp two-side Gaussian bounds for the heat kernel that sharpens the well-known Li-Yau’s two-side bounds, based on the sharp Li-Yau’s Harnack inequality on such a manifold. As an application, we get the optimal gradient and Laplacian...