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We consider the evolution of the asymptotically hyperbolic mass under the curvature-normalized Ricci flow of asymptotically hyperbolic, conformally compactifiable manifolds. In contrast to asymptotically flat manifolds, for which ADM mass…

Differential Geometry · Mathematics 2015-05-30 T. Balehowsky , E. Woolgar

Hyperbolic spaces have recently gained momentum in the context of machine learning due to their high capacity and tree-likeliness properties. However, the representational power of hyperbolic geometry is not yet on par with Euclidean…

Machine Learning · Computer Science 2018-06-29 Octavian-Eugen Ganea , Gary Bécigneul , Thomas Hofmann

We introduce the notions of `super-Ricci flows' and `Ricci flows' for time-dependent families of metric measure spaces $(X,d_t,m_t)_{t\in I}$. The former property is proven to be stable under suitable space-time versions of mGH-convergence.…

Differential Geometry · Mathematics 2017-08-10 Karl-Theodor Sturm

Representing graphs as sets of node embeddings in certain curved Riemannian manifolds has recently gained momentum in machine learning due to their desirable geometric inductive biases, e.g., hierarchical structures benefit from hyperbolic…

Machine Learning · Computer Science 2020-06-09 Calin Cruceru , Gary Bécigneul , Octavian-Eugen Ganea

The Ricci flow has been of fundamental importance in mathematics, most famously though its use as a tool for proving the Poincar\'e Conjecture and Thurston's Geometrization Conjecture. It has a parallel life in physics, arising as the first…

Differential Geometry · Mathematics 2013-12-23 Karsten Gimre , Christine Guenther , James Isenberg

An extrinsic representation of a Ricci flow on a differentiable n-manifold M is a family of submanifolds S(t), each smoothly embedded in R^{n+k}, evolving as a function of time t such that the metrics induced on the submanifolds S(t) by the…

Differential Geometry · Mathematics 2013-11-05 Vincent Coll , Jeff Dodd , David L. Johnson

We consider the Ricci flow equation for invariant metrics on compact and connected homogeneous spaces whose isotropy representation decomposes into two irreducible inequivalent summands. By studying the corresponding dynamical system, we…

Differential Geometry · Mathematics 2012-09-17 Maria Buzano

We prove dynamical stability and instability theorems for asymptotically hyperbolic static solutions of Einstein's equation with $\Lambda<0$, viewed as self-similar solutions of the Ricci-harmonic flow. More precisely, we show that static…

Differential Geometry · Mathematics 2026-04-27 Rasmus Jouttijärvi , Klaus Kroencke , Louis Yudowitz

Following work of Ecker, we consider a weighted Gibbons-Hawking-York functional on a Riemannian manifold-with-boundary. We compute its variational properties and its time derivative under Perelman's modified Ricci flow. The answer has a…

Differential Geometry · Mathematics 2015-05-28 John Lott

The Poincar\'e problem is a model of two-dimensional internal waves in stable-stratified fluid. The chess billiard flow, a variation of a typical billiard flow, drives the formation behind and describes the evolution of these internal…

Analysis of PDEs · Mathematics 2022-10-25 Sally Zhu , Zhenhao Li

If we want to deform a compact Riemannian manifold with boundary using Ricci flow, we first need to decide on appropriate boundary conditions. We would like these conditions to reflect the geometric nature of the flow and allow for a…

Differential Geometry · Mathematics 2024-03-15 Rasmus Jouttijärvi

This paper studies the Ricci flow on closed manifolds admitting harmonic spinors. It is shown that Perelman's Ricci flow entropy can be expressed in terms of the energy of harmonic spinors in all dimensions, and in four dimensions, in terms…

Differential Geometry · Mathematics 2022-10-26 Julius Baldauf

We introduce a new parabolic flow deforming any Riemannian metric on a spin manifold by following a constrained gradient flow of the total scalar curvature. This flow is built out of the well-known Dirac-Einstein functional. We prove local…

Analysis of PDEs · Mathematics 2024-09-20 Yannick Sire , Tian Xu

Modern machine learning increasingly leverages the insight that high-dimensional data often lie near low-dimensional, non-linear manifolds, an idea known as the manifold hypothesis. By explicitly modeling the geometric structure of data…

Machine Learning · Computer Science 2026-03-02 Willem Diepeveen , Deanna Needell

A hyperbolic space has been shown to be more capable of modeling complex networks than a Euclidean space. This paper proposes an explicit update rule along geodesics in a hyperbolic space. The convergence of our algorithm is theoretically…

Machine Learning · Statistics 2018-05-29 Yosuke Enokida , Atsushi Suzuki , Kenji Yamanishi

We design strategies in nonlinear geometric analysis to temper the effects of adversarial learning for sufficiently smooth data of numerical method-type dynamics in encoder-decoder methods, variational and deterministic, through the use of…

Numerical Analysis · Mathematics 2026-05-29 Andrew Gracyk

The main objective of this thesis is the study of the evolution under the Ricci flow of surfaces with singularities of cone type. A second objective, emerged from the techniques we use, is the study of families of Ricci flow solitons in…

Differential Geometry · Mathematics 2017-07-06 Daniel Ramos

We prove a curvature pinching result for the Ricci flow on asymptotically flat manifolds: if an asymptotically flat manifold of dimension $n\geq 3$ has scale-invariant integral norm of curvature sufficiently pinched relative to the inverse…

Differential Geometry · Mathematics 2019-08-01 Eric Chen

We review recent results on the study of the isoperimetric problem on Riemannian manifolds with Ricci lower bounds. We focus on the validity of sharp second order differential inequalities satisfied by the isoperimetric profile of possibly…

Differential Geometry · Mathematics 2023-05-16 Marco Pozzetta

Learning task-specific representations of persistence diagrams is an important problem in topological data analysis and machine learning. However, current state of the art methods are restricted in terms of their expressivity as they are…

Machine Learning · Computer Science 2021-03-18 Panagiotis Kyriakis , Iordanis Fostiropoulos , Paul Bogdan
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