Mesure (mathématiques) | EPFL Graph Search

En mathématiques, une mesure positive (ou simplement mesure quand il n'y a pas de risque de confusion) est une fonction qui associe une grandeur numérique à certains sous-ensembles d'un ensemble donné. Il s'agit d'un important concept en analyse et en théorie des probabilités. Intuitivement, la mesure d'un ensemble ou sous-ensemble est similaire à la notion de taille, ou de cardinal pour les ensembles discrets. Dans ce sens, la mesure est une généralisation des concepts de longueur, aire ou volume dans des espaces de dimension 1, 2 ou 3 respectivement. L'étude des espaces munis de mesures est l'objet de la théorie de la mesure. Définition Terminologies connexes

Lorsqu'on dispose d'une mesure μ sur un espace mesurable (X,\mathcal{A}), on dit que le triplet (X,\mathcal{A},\mu) est un espace mesuré ;
Pour S ensemble mesurable (c'est-à-dire pour S\in\mathcal{A}), la valeur μ(S) est appelée la mesure de S ;
Lorsque μ(

Supramenable groups

Julian Kellerhals

In this thesis we investigate the class of supramenable groups. In the first part we give an overview of some analogous characterizations of amenable and supramenable groups. This is followed by the study of two properties close to supramenability: megamenability, which is a stronger property, and a Reiter condition that turns out to be equivalent to sub-exponential growth. In chapter three we present an argument showing why supramenable groups are precicely the groups not admitting an injective Lipschitz embedding of a free group. In the fourth and last chapter we give a characterization by a measure property before concluding with a fixed point property.

EPFL2014

Optimal control of geodesics in Riemannian manifolds

Roland Rozsnyo

The aim of this dissertation is to solve numerically the following problem, denoted by P : given a Riemannian manifold and two points a and b belonging to that manifold, find a tangent vector T at a, such that expa(T) = b, assuming that T exists. This problem is set under an optimal control formulation, which requires the definition of an objective function and a space of control, the choice of a method for the calculation of the descent direction of that function in the space of control and the use of an optimization algorithm to find its minimum, which corresponds to the solution of the original problem by construction. Several techniques are necessary to be put together, coming from the fields of geometry, numerical analysis and optimization. The first part will concern a recalling of the mathematical context in which this formulation takes place. The general principles of optimal control will also be given. In the second part, we will present an intrinsic formulation of the optimal control problem associated to P, based on Jacobi fields, which will play the role of the so called adjoint state. This derivation leads to necessary optimality conditions. We will illustrate explicitly that formulation by treating the specific case of Riemannian manifolds with constant sectional curvature. Then, we will derive the optimal control problem in coordinates, not only to check the intrinsic formulation but also to reveal how it is hidden behind the expressions in coordinates. Their use reveals some quantities whose interpretation may be given this way. Moreover, we will show that more possibilities exist to chose the cost function and the control space in coordinates. In a second step, an alternative approach will consider the Hamiltonian formulation of geodesics. This is an incursion into symplectic geometry. We will then reformulate the Riemannian optimal control problem in its Hamiltonian version. In the third part, the numerical methods used for solving P will be presented. The discretization imposes the definition of new discrete optimal control problems. The technique shows that the discrete adjoint state equation strongly depends on the numerical scheme used to solve the direct problem. We will give a collection of numerical computations in the specific case of parametric piece of surfaces, where the surface can be defined by one or several Bézier patches, each one corresponding to a chart, which is representative of a Riemannian manifold. We will compare the different numerical approaches. The last but one part will be devoted to the interesting application of wooden roof building, where the structure is made of wooden boards, with geodesic trajectories on the designed piece of surface. The Geos (Geodesic solver) software has been developed for that purpose. After having introduced some specific numerical methods used in the code, we present the Geos application interface (AI) developed as a tool for the conception of such a roof. We then show an existing wooden structure built according to that mean. Finally, we will summarize the results of our research and discuss future possible prospects.

EPFL2007

Inferring the flow properties of epithelial tissues from their geometry

Valentin Camille Druelle, Marko Popovic, Matthieu Wyart

Amorphous materials exhibit complex material properties with strongly nonlinear behaviors. Below a yield stress they behave as plastic solids, while they start to yield above a critical stress sigma(c). A key quantity controlling plasticity which is, however, hard to measure is the density P(x) of weak spots, where x is the additional stress required for local plastic failure. In the thermodynamic limit P(x) similar to x(theta) is singular at x = 0 in the solid phase below the yield stress sigma(c). This singularity is related to the presence of system spanning avalanches of plastic events. Here we address the question if the density of weak spots and the flow properties of a material can be determined from the geometry of an amorphous structure alone. We show that a vertex model for cell packings in tissues exhibits the phenomenology of plastic amorphous systems. As the yield stress is approached from above, the strain rate vanishes and the avalanches size S and their duration tau diverge. We then show that in general, in materials where the energy functional depends on topology, the value x is proportional to the length L of a bond that vanishes in a plastic event. For this class of models P(x) is therefore readily measurable from geometry alone. Applying this approach to a quantification of the cell packing geometry in the developing wing epithelium of the fruit fly, we find that in this tissue P(L) exhibits a power law with exponents similar to those found numerically for a vertex model in its solid phase. This suggests that this tissue exhibits plasticity and non-linear material properties that emerge from collective cell behaviors and that these material properties govern developmental processes. Our approach based on the relation between topology and energetics suggests a new route to outstanding questions associated with the yielding transition.