The stochastic renormalized mean curvature flow for planar convex sets
Marc Arnaudon (IMB), Kol\'eh\`e Coulibaly-Pasquier (IECL), Laurent, Miclo (TSE-R)

TL;DR
This paper studies a stochastic curvature flow for convex sets in the plane, proving convergence to disks, analyzing properties of the flow under noise, and establishing new geometric inequalities and invariance conditions.
Contribution
It introduces the stochastic renormalized curvature flow (SRCF), proves its properties, and provides the first example of an infinite lifetime SRCF not reducible to finite dimensions.
Findings
RCF entropy and perimeter-to-volume ratio decrease over time
SRCF can have infinite lifetime under certain symmetry conditions
New isoperimetric estimate related to skeleton complexity
Abstract
We investigate renormalized curvature flow (RCF) and stochastic renormalized curvature flow (SRCF) for convex sets in the plane.RCF is the gradient descent flow for logarithm of where is the perimeter and is the volume. SRCF is RCF perturbated by a Brownian noise and has the remarkable property that it can be intertwined with the Brownian motion, yielding a generalization of Pitman "2M-X" theorem. We prove that along RCF, entropy for curvature as well as are non-increasing. We deduce infinite lifetime and convergence to a disk after normalization.For SRCF the situation is more complicated. The process is always a supermartingale. For to be a supermartingale, we need that the starting set is invariant by the isometry group generated by the reflection with respect to the…
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Taxonomy
TopicsGeometric Analysis and Curvature Flows · Point processes and geometric inequalities · Geometry and complex manifolds
