Sous-espace vectoriel

Résumé

En algèbre linéaire, un sous-espace vectoriel d'un espace vectoriel E, est une partie non vide F, de E, stable par combinaisons linéaires. Cette stabilité s'exprime par : *la somme de deux vecteurs de F appartient à F ; *le produit d'un vecteur de F par un scalaire appartient à F. Muni des lois induites, F est alors un espace vectoriel. L'intersection d'une famille non vide de sous-espaces de E est un sous-espace de E. La réunion d'une famille non vide de sous-espaces n'en est généralement pas un ; le sous-espace engendré par cette réunion est la somme de cette famille. Définitions équivalentes Soit E un espace vectoriel sur un corps K. En effet, la condition 1, plus forte que la condition « F est non vide et stable par sommes », lui est équivalente en présence de la condition 2 car cette dernière entraîne que F est stable par opposés (si alors –u = (–1)∙u ∈ F). Une caractérisation intermédiaire donc également équivalente est : Par ailleurs, la stabilité par combina

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The objective of this PhD thesis is the approximate computation of the solutions of the Spectral Problem associated with the Laplace operator on a compact Riemann surface without boundaries. A Riemann surface can be seen as a gluing of portions of the Hyperbolic Plane made with suitable conditions to obtain a 2 dimensional manifold. The solutions of the Spectral Problem associated with the Laplace operator are to be understood as the eigenfunctions defined on the surface and their corresponding eigenvalues. This work is separated into two parts: the first part describes the method used to approximate the eigenvalues and eigenfunctions, the second focuses on the design of a program to compute these approximations. The approximation method is inspired by the Finite Element Method (FEM), in that it relies on the variational expression of the Spectral Problem and the definition of a finite subspace of functions in which the approximated eigenvalues and eigenfunctions are computed. However, it differs from the FEM in that it removes the euclidian basis of the FEM and is invariant under the isometries of the Hyperbolic Plane. To ful fill this objective, we begin by geodesically triangulating the surface as regularly as possible. This hyperbolic triangulation allows us to de ne the finite subspace of functions by using the concept of barycentric coordinates associated with each vertex of the triangulation (idea introduced by Whitney and taken up by Dodziuk). We then prove that the approximated solutions convergence to the exact ones when the diameter of the triangulation decreases, as well as the order of convergence. The program is a practical application of the theoretical work and allows the computation of the approximated eigenfunctions and eigenvalues.

EPFL2012

Chargement

Vacuum Stability in Supersymmetric Effective Theories

Leonardo Brizi

The main topics discussed in this thesis are supersymmetric low-energy effective theories and metastability conditions in generic non-renormalizable models with global and local supersymmetry. In the first part we discuss the conditions under which the low-energy expansion in space-time derivatives preserves supersymmetry implying that heavy multiplets can be more efficiently integrated out directly at the superfield level. These conditions translate into the requirements that also fermions and auxiliary fields should be small compared to the heavy mass scale. They apply not only to the matter sector, but also to the gravitational one if present, and imply in that case that the gravitino mass should be small. We finally give a simple prescription to integrate out heavy chiral and vector superfields consisting respectively in imposing stationarity of the superpotential and of the Kähler potential; the procedure holds in the same form both for global and local supersymmetry. In the second part we study general criteria for the existence of metastable vacua which break global supersymmetry in models with local gauge symmetries. In particular we present a strategy to define an absolute upper bound on the mass of the lightest scalar field which depends on the geometrical properties of the Kähler target manifold. This bound can be saturated by properly tuning the superpotential and its positivity therefore represents a necessary and sufficient condition for the existence of metastable vacua. It is derived by looking at the subspace of all those directions in field space for which an arbitrary supersymmetric mass term is not allowed and scalar masses are controlled by supersymmetry-breaking splitting effects. This subspace includes not only the direction of supersymmetry breaking, but also the directions of gauge symmetry breaking and the lightest scalar is in general a linear combination of fields spanning all these directions. Our purpose is to show that the largest value for the lightest mass is in general achieved when the lightest scalar is a combination of the Goldstone and the Goldstino partners. We conclude by computing the effects induced by the integration of heavy multiplets on the light masses. In particular we focus on the sGoldstino partners and we show that heavy chiral multiplets induce a negative level-repulsion effect that tends to compromise vacuum stability, whereas heavy vector multiplets in general induce a positive-definite contribution. Our results find application in the context of string-inspired supergravity models, where metastability conditions can be used to discriminate among different compactification scenarios and supersymmetric effective theories can be used to face the problem of moduli stabilization.

EPFL2011

Chargement

, , ,

In this work, we present a PDE-aware deep learning model for the numerical solution to the inverse problem of electrocardiography. The model both leverages data availability and exploits the knowledge of a physically based mathematical model, expressed by means of partial differential equations (PDEs), to carry out the task at hand. The goal is to estimate the epicardial potential field from measurements of the electric potential at a discrete set of points on the body surface. The employment of deep learning techniques in this context is made difficult by the low amount of clinical data at disposal, as measuring cardiac potentials requires invasive procedures. Suitably exploiting the underlying physically based mathematical model allowed circumventing the data availability issue and led to the development of fast-training and low-complexity models. Physical awareness has been pursued by means of two elements: the projection of the epicardial potential onto a space-time reduced subspace, spanned by the numerical solutions of the governing PDEs, and the inclusion of a tensorial reduced basis solver of the forward problem in the network architecture. Numerical tests have been conducted only on synthetic data, obtained via a full order model approximation of the problem at hand, and two variants of the model have been addressed. Both proved to be accurate, up to an average

\ell^1

-norm relative error on epicardial activation maps of 3.5%, and both could be trained in

\approx$$15

min. Nevertheless, some improvements, mostly concerning data generation, are necessary in order to bridge the gap with clinical applications.

2022

Chargement

, , ,

\ell^1

-norm relative error on epicardial activation maps of 3.5%, and both could be trained in

\approx$$15

min. Nevertheless, some improvements, mostly concerning data generation, are necessary in order to bridge the gap with clinical applications.

2022