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An isometry is a geometric transformation that preserves distances between pairs of points. We present methods to classify isometries in the Euclidean plane, and extend these methods to spherical, single elliptical, and hyperbolic geometry.…

Metric Geometry · Mathematics 2023-06-28 Lillian MacArthur , Honglin Zhu

Let $\varUpsilon$ be the configuration space over a complete and separable metric base space, endowed with the Poisson measure $\pi$. We study the geometry of $\varUpsilon$ from the point of view of optimal transport and Ricci-lower bounds.…

Probability · Mathematics 2025-01-22 Lorenzo Dello Schiavo , Ronan Herry , Kohei Suzuki

We study isometric embeddings of a Euclidean space or a Heisenberg group into a higher dimensional Heisenberg group, where both the source and target space are equipped with an arbitrary left-invariant homogeneous distance that is not…

Metric Geometry · Mathematics 2017-11-27 Zoltán M. Balogh , Katrin Fässler , Hernando Sobrino

This paper investigates which smooth manifolds arise as quotients (orbit spaces) of flows of vector fields. Such quotient maps were already known to be surjective on fundamental groups, but this paper shows that every epimorphism of…

Dynamical Systems · Mathematics 2017-03-14 Robert E. Gompf

We introduce a new class of surfaces in Euclidean $3$-space, called surfaces of osculating circles, using the concept of osculating circle of a regular curve. These surfaces contain a uniparametric family of planar lines of curvature. In…

Differential Geometry · Mathematics 2021-12-08 Rafael López , Cetin Camci , Ali Ucum , Kazim Ilarslan

Consider a set of points sampled independently near a smooth compact submanifold of Euclidean space. We provide mathematically rigorous bounds on the number of sample points required to estimate both the dimension and the tangent spaces of…

Statistics Theory · Mathematics 2023-09-26 Uzu Lim , Harald Oberhauser , Vidit Nanda

We analyse second order (in Riemann curvature) geometric flows (un-normalised) on locally homogeneous three manifolds and look for specific features through the solutions (analytic whereever possible, otherwise numerical) of the evolution…

Differential Geometry · Mathematics 2015-04-13 Sanjit Das , Kartik Prabhu , Sayan Kar

Euclidean distance geometry is the study of Euclidean geometry based on the concept of distance. This is useful in several applications where the input data consists of an incomplete set of distances, and the output is a set of points in…

Quantitative Methods · Quantitative Biology 2012-05-03 Leo Liberti , Carlile Lavor , Nelson Maculan , Antonio Mucherino

We study surfaces with a constant ratio of principal curvatures in Euclidean and simply isotropic geometries and characterize rotational, channel, ruled, helical, and translational surfaces of this kind under some technical restrictions…

Differential Geometry · Mathematics 2025-10-17 Khusrav Yorov , Mikhail Skopenkov , Helmut Pottmann

We present a simple approach to study the one-dimensional pressureless Euler system via adhesion dynamics in the Wasserstein space of probability measures with finite quadratic moments. Starting from a discrete system of a finite number of…

Analysis of PDEs · Mathematics 2014-09-16 Luca Natile , Giuseppe Savaré

Borel probability measures living on metric spaces are fundamental mathematical objects. There are several meaningful distance functions that make the collection of the probability measures living on a certain space a metric space. We are…

Functional Analysis · Mathematics 2018-06-14 Dániel Virosztek

Streamlines of a relativistic perfect isentropic fluid are geodesics of a Riemannian space whose metric is defined by enthalpy of the fluid. This fact simplifies the solution of some problems, as is also of interest from the point of view…

General Relativity and Quantum Cosmology · Physics 2013-11-19 Leonid Verozub

A Wasserstein spaces is a metric space of sufficiently concentrated probability measures over a general metric space. The main goal of this paper is to estimate the largeness of Wasserstein spaces, in a sense to be precised. In a first…

Metric Geometry · Mathematics 2012-07-17 Benoit Kloeckner

It has been shown beneficial for many types of data which present an underlying hierarchical structure to be embedded in hyperbolic spaces. Consequently, many tools of machine learning were extended to such spaces, but only few…

Machine Learning · Computer Science 2023-06-27 Clément Bonet , Laetitia Chapel , Lucas Drumetz , Nicolas Courty

In this paper we investigate the metric properties of quadrics and cones of the $n$-dimensional Euclidean space. As applications of our formulas we give a more detailed description of the construction of Chasles and the wire model of…

Metric Geometry · Mathematics 2017-07-06 Ákos G. Horváth

Wasserstein distance induces a natural Riemannian structure for the probabilities on the Euclidean space. This insight of classical transport theory is fundamental for tremendous applications in various fields of pure and applied…

Probability · Mathematics 2023-08-14 Daniel Bartl , Mathias Beiglböck , Gudmund Pammer

We give a metric characterization of snowflakes of Euclidean spaces. Namely, a metric space is isometric to $\mathbb R^n$ equipped with a distance $(d_{\rm E})^\epsilon$, for some $n\in \mathbb N_0$ and $\epsilon\in (0,1]$, where $d_{\rm…

Metric Geometry · Mathematics 2014-10-01 Kyle Kinneberg , Enrico Le Donne

The classification of the possible equilibrium shapes that a self-gravitating fluid can take in a Riemannian manifold is a classical problem in mathematical physics. In this paper it is proved that the equilibrium shapes are isoparametric…

Mathematical Physics · Physics 2015-06-26 Daniel Peralta-Salas

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…

Differential Geometry · Mathematics 2025-04-11 Marc Arnaudon , Xue-Mei Li , Benedikt Petko

This is an expository paper on the theory of gradient flows, and in particular of those PDEs which can be interpreted as gradient flows for the Wasserstein metric on the space of probability measures (a distance induced by optimal…

Analysis of PDEs · Mathematics 2016-09-14 Filippo Santambrogio