Paper · Mar 27, 2018
Neck Detection for Two-Convex Hypersurfaces Embedded in Euclidean Space undergoing Brendle-Huisken G-Flow
As on arXiv
Abstract
Recently Brendle-Huisken introduced a fully nonlinear flow . Their aim was to extend the surgery algorithm of Huisken-Sinestrari, into the Riemannian setting. The aim of this paper is to go through the details on how to perform neck detection for a closed, embedded hypersurface in undergoing this -flow. In order to do this we make some adjustments to Brendle and Huiskens gradient estimate, after we have done this we can go on to argue as in Huisken-Sinestrari's paper Mean curvature flow with surgeries of two-convex hyperusrfaces, in order to classify two-convex surfaces undergoing -flow.
- Submitted
- Mar 27, 2018
- Subjects
- math.DG
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Read around it
Background reading
The guides for the topics of this paper: what each idea is, why it matters, and the papers to read first.
In the guidebook
- 11B.7Hamilton’s Program in 2002
What was proved, what was missing, and where Perelman came in.
- 12B.5The Canonical Neighbourhood Theorem
Every high-curvature point looks like a neck or a cap.
- 12B.7The Singular Time: Horns and Strong Necks
The limit metric at a singular time, the set Ω where curvature stays bounded, ε-horns and their ends, and strong δ-necks deep in a horn, following Perelman II §3 and Morgan–Tian chapter 11.