The fracture of brittle materials is of utmost importance for civil engineering and seismology applications. A different approach towards the aim of early identification of fracture and the prediction of failure before it occurs is attempted in this work. Laboratory experiments were conducted in a variety of rock and cement based material specimens of various shapes and sizes. The applied loading schemes were cyclic or increasing and the specimens were tested to compression and bending type loading of various levels. The techniques of Pressure Stimulated Current and Bending Stimulated Current were used for the detection of electric signal emissions during the various deformation stages of the specimens. The detected signals were analysed macroscopically and microscopically so as to find suitable criteria for fracture prediction and correlation between the electrical and mechanical parameters. The macroscopic proportionality of the mechanically stimulated electric signal and the strain was experimentally verified, the macroscopic trends of the PSC and BSC electric signals were modelled and the effects of material memory to the electric signals were examined. The current of a time-varying RLC electric circuit was tested against experimental data with satisfactory results and it was proposed as an electrical equivalent model. Wavelet based analysis of the signal revealed the correlation between the frequency components of the electric signal and the deformation stages of the material samples. Especially the increase of the high frequency component of the electric signal seems to be a good precursor of macrocracking initiation point. The additional electric stimulus of a dc voltage application seems to boost the frequency content of the signal and reveals better the stages of cracking process. The microscopic analysis method is scale-free and thus it can confront with the problems of size effects and material properties effects. The AC conductivity time series of fractured and pristine specimens were also analysed by means of wavelet transform and the spectral analysis was used to differentiate between the specimens. A non-destructive technique may be based on these results. Analysis has shown that the electric signal perturbation is an indicator of the forthcoming fracture, as well as of the fracture that has already occurred in specimens.

The presence of different types of defects is an important reality for manufacturers and users of engineering materials. Generally, the defects are either considered to be the unwanted products of impurities in the raw materials or to have been introduced during the manufacturing process. In high-quality steel materials, such as tool steel, the defects are usually non-metallic inclusions such as oxides or sulfides. Traditional methods for purity control during standard manufacturing practice are usually based on the light optical microscopy scanning of polished surfaces and some statistical evaluation of the results. Yet, as the steel manufacturing process has improved, large defects have become increasingly rare. A major disadvantage of the traditional quality control methods is that the accuracy decreases proportionally to the increased rarity of the largest defects unless large areas are examined. However, the use of very high cycle fatigue to 109 cycles has been shown to be a powerful method to locate the largest defects in steel samples. The distribution of the located defects may then be modelled using extreme value statistics. This work presents new methods for determining the volume distribution of large defects in high-quality steels, based on ultrasonic fatigue and the Generalized Extreme Value (GEV) distribution. The methods have been developed and verified by extensive experimental testing, including over 400 fatigue test specimens. Further, a method for reducing the distributions into one single ranking variable has been proposed, as well as a way to estimate an ideal endurance strength at different life lengths using the observed defects and endurance limits. The methods can not only be used to discriminate between different materials made by different process routes, but also to differentiate between different batches of the same material. It is also shown that all modes of the GEV are to be found in different steel materials, thereby challenging a common assumption that the Gumbel distribution, a special case of the GEV, is the appropriate distribution choice when determining the distribution of defects. The new methods have been compared to traditional quality control methods used in common practice (surface scanning using LOM/SEM and ultrasound C-scan), and suggest a greater number of large defects present in the steel than could otherwise be detected.

Le sujet principal de cette thèse est la physique des verres de spin. Les verres de spin ont été introduits au début des années 70 pour décrire alliages magnétiques diluées. Ils ont désormais été considerés pour comprendre le comportement de liquides sousrefroidis. Parmis les systèmes qui peuvent être décrits par le langage des systèmes desordonnés, on trouve les problèmes d'optimisation combinatoire. Dans la première partie de cette thèse, nous considérons les modèles de verre de spin avec intéraction de Kac pour investiguer la phase de basse température des liquides sous-refroidis. Dans les chapitres qui suivent, nous montrons comment certaines caractéristiques des modèles de verre de spin peuvent être obtenues à partir de résultats de la théorie des matrices aléatoires en connection avec la statistique des valeurs extrêmes. Dans la dernière partie de la thèse, nous considérons la connexion entre la théorie desverres de spin et la science computationnelle, et présentons un nouvel algorithme qui peut être appliqué à certains problèmes dans le domaine des finances.

We employ observational data about the largest cosmic void and most massive galaxy cluster known to date, the 'Cold Spot' void and the 'El Gordo' cluster, in order to constrain the parameter |fR0| from the f(R) gravity formulation by Hu and Sawicki and the matter power spectrum normalization at present time, σ8. We obtain the marginalized posterior distribution for these two parameters through a Markov Chain Monte Carlo analysis, where the likelihood function is modeled through extreme value statistics. The prior distribution for the additional cosmological parameters included in the computations (Ωdmh2, Ωbh2, h and ns) is matched to recent constraints. By combining the likelihood functions for both voids and clusters, we obtain a mean value log|fR0| = -5.1 ± 1.6, which is compatible with General Relativity (log|fR0| ≤-8) at 95% confidence level, but suggests a preference for a non-negligible modified gravity correction.

La prévision d'événements extrêmes est une question cruciale dans des domaines divers allant de la météorologie à la finance. Trois classes d'universalité (Gumbel, Fréchet et Weibull) ont été identifiées pour des variables aléatoires indépendantes et de distribution identique (i.i.d.).La modélisation par des variables aléatoires i.i.d., notamment avec le modèle d'énergie aléatoire de Derrida, a permis d'améliorer la compréhension des systèmes désordonnés. Cette hypothèse n'est toutefois pas valide pour de nombreux systèmes physiques qui présentent de fortes corrélations. Dans cette thèse, nous étudions trois modèles physiques de variables aléatoires fortement corrélées : des fermions piégés,des matrices aléatoires et des marches aléatoires. Dans la première partie, nous montrons plusieurs correspondances exactes entre l'état fondamental d'un gaz de Fermi piégé et des ensembles de matrices aléatoires. Le gaz Fermi est inhomogène dans le potentiel de piégeage et sa densité présente un bord fini au-delà duquel elle devient essentiellement nulle. Nous développons une description précise des statistiques spatiales à proximité de ce bord, qui va au-delà des approximations semi-classiques standards (telle que l'approximation de la densité locale). Nous appliquons ces résultats afin de calculer les statistiques de la position du fermion le plus éloigné du centre du piège, le nombre de fermions dans un domaine donné (statistiques de comptage) et l'entropie d'intrication correspondante. Notre analyse fournit également des solutions à des problèmes ouverts de valeurs extrêmes dans la théorie des matrices aléatoires. Nous obtenons par exemple une description complète des fluctuations de la plus grande valeur propre de l'ensemble complexe de Ginibre.Dans la deuxième partie de la thèse, nous étudions les questions de valeurs extrêmes pour des marches aléatoires. Nous considérons les statistiques d'écarts entre positions maximales consécutives (gaps), ce qui nécessite de prendre en compte explicitement le caractère discret du processus. Cette question ne peut être résolue en utilisant la convergence du processus avec son pendant continu, le mouvement Brownien. Nous obtenons des résultats analytiques explicites pour ces statistiques de gaps lorsque la distribution de sauts est donnée par la loi de Laplace et réalisons des simulations numériques suggérant l'universalité de ces résultats. / Predicting the occurrence of extreme events is a crucial issue in many contexts, ranging from meteorology to finance. For independent and identically distributed (i.i.d.) random variables, three universality classes were identified (Gumbel, Fréchet and Weibull) for the distribution of the maximum. While modelling disordered systems by i.i.d. random variables has been successful with Derrida's random energy model, this hypothesis fail for many physical systems which display strong correlations. In this thesis, we study three physically relevant models of strongly correlated random variables: trapped fermions, random matrices and random walks.In the first part, we show several exact mappings between the ground state of a trapped Fermi gas and ensembles of random matrix theory. The Fermi gas is inhomogeneous in the trapping potential and in particular there is a finite edge beyond which its density vanishes. Going beyond standard semi-classical techniques (such as local density approximation), we develop a precise description of the spatial statistics close to the edge. This description holds for a large universality class of hard edge potentials. We apply these results to compute the statistics of the position of the fermion the farthest away from the centre of the trap, the number of fermions in a given domain (full counting statistics) and the related bipartite entanglement entropy. Our analysis also provides solutions to open problems of extreme value statistics in random matrix theory. We obtain for instance a complete description of the fluctuations of the largest eigenvalue in the complex Ginibre ensemble.In the second part of the thesis, we study extreme value questions for random walks. We consider the gap statistics, which requires to take explicitly into account the discreteness of the process. This question cannot be solved using the convergence of the process to its continuous counterpart, the Brownian motion. We obtain explicit analytical results for the gap statistics of the walk with a Laplace distribution of jumps and provide numerical evidence suggesting the universality of these results.

Une interface est une zone de l'espace qui sépare deux régions possédant des propriétés physiques différentes. La plupart des interfaces de la nature résultent d'un processus de croissance, mêlant une composante aléatoire et une dynamique déterministe régie par les symétries du problème. Le résultat du processus de croissance est un objet présentant des corrélations à longue portée. Dans cette thèse, nous nous proposons d'étudier la statistique d'extrême de différents types d'interfaces. Une première motivation est de raffiner la compréhension géométrique de tels objets, via leur maximum. Une seconde motivation s'inscrit dans la démarche plus générale de la statistique d'extrême de variables aléatoires fortement corrélées. A l'aide de méthodes analytiques d'intégrales de chemin nous analysons la distribution du maximum d'interfaces à l'équilibre, dont l'énergie es t purement élastique à courte portée. Nous attaquons ensuite le problème d'interfaces élastiques en milieu désordonné, principalement à l'aide de simulations numériques. Enfin nous étudierons une interface hors-équilibre dans son régime de croissance. L'équivalence de ce type d'interface avec le polymère dirigé en milieu aléatoire, un des paradigmes de la physique statistique des systèmes désordonnés, donne une portée étendue aux résultats concernant la statistique du maximum de l'interface. Nous exposerons les résultats que nous avons obtenus sur un modèle de mouvements browniens qui ne se croisent pas, tout en explicitant le lien entre ce modèle, l'interface en croissance et le polymère dirigé. / An interface is an area of space that separates two regions having different physical properties. Most interfaces in nature are the result of a growth process, mixing a random behavior and a deterministic dynamic derived from the symmetries of the problem. This growth process gives an object with extended correlations. In this thesis, we focus on the study of the extremum of different kinds of interfaces. A first motivation is to refine the geometric properties of such objects, looking at their maximum. A second motivation is to explore the extreme value statistics of strongly correlated random variables. Using path integral techniques we analyse the probability distribution of the maximum of equilibrium interfaces, possessing short range elastic energy. We then extend this to elastic interfaces in random media, with essentially numerical simulations. Finally we study a particular type of out-of-equilibrium interface, in its growing regime. Such interface is equivalent to the directed polymer in random media, a paradigm of the statistical mechanics of disordered systems. This equivalence reinforces the interest in the extreme value statistics of the interface. We will show the exact results we obtained for a non-intersecting Brownian motion model, explaining precisely the link with the growing interface and the directed polymer.

[pt] Com diversas aplicações em matemática, física e finanças, Teoria das Matrizes Aleatórias (RMT) recentemente atraiu muita atenção. Enquanto o RMT Hermitiano é de especial importância na física por causa da Hermenticidade de operadores associados a observáveis em mecânica quântica, O RMT não-Hermitiano também atraiu uma atenção considerável, em particular porque eles podem ser usados como modelos para sistemas físicos dissipativos ou abertos. No entanto, devido à ausência de uma simetria simplificada, o estudo de matrizes aleatórias não-Hermitianas é, em geral, uma tarefa difícil. Um subconjunto especial de matrizes aleat órias não-Hermitianas, as chamadas matrizes aleatórias normais, são modelos interessantes a serem considerados, uma vez que oferecem mais simetria, tornando-as mais acessíveis às investigções analíticas. Por definição, uma matriz normal M é uma matriz quadrada que troca com seu adjunto Hermitiano. Nesta tese, amplicamos a derivação de estatísticas de valores extremos (EVS) de matrizes aleatórias Hermitianas, com base na abordagem de polinômios ortogonais, em matrizes aleatórias normais e em gases Coulomb 2D em geral. A força desta abordagem a sua compreensão física e intuitiva. Em primeiro lugar, essa abordagem fornece uma derivação alternativa de resultados na literatura. Precisamente falando, mostramos a convergência do autovalor redimensionado com o maior módulo de um conjunto de Ginibre para uma distribuição de Gumbel, bem como a universalidade para um potencial arbitrário radialmente simtérico que atenda certas condições. Em segundo lugar, mostra-se que esta abordagem pode ser generalizada para obter a convergência do autovalor com menor módulo e sua universalidade no limite interno finito do suporte do autovalor. Um aspecto interessante deste trabalho é o fato de que podemos usar técnicas padrão de matrizes aleatórias Hermitianas para obter o EVS de matrizes aleatórias não Hermitianas. / [en] With diverse applications in mathematics, physics, and finance, Random Matrix Theory (RMT) has recently attracted a great deal of attention. While Hermitian RMT is of special importance in physics because of the Hermiticity of operators associated with observables in quantum mechanics, non-Hermitian RMT has also attracted a considerable attention, in particular because they can be used as models for dissipative or open physical systems. However, due to the absence of a simplifying symmetry, the study of non-Hermitian random matrices is, in general, a diffcult task. A special subset of non-Hermitian random matrices, the so-called random normal matrices, are interesting models to consider, since they offer more symmetry, thus making them more amenable to analytical investigations. By definition, a normal matrix M is a square matrix which commutes with its Hermitian adjoint, i.e., (M, M (1)). In this thesis, we present a novel derivation of extreme value statistics (EVS) of Hermitian random matrices, namely the approach of orthogonal polynomials, to normal random matrices and 2D Coulomb gases in general. The strength of this approach is its physical and intuitive understanding. Firstly, this approach provides an alternative derivation of results in the literature. Precisely speaking, we show convergence of the rescaled eigenvalue with largest modulus of a Ginibre ensemble to a Gumbel distribution, as well as universality for an arbitrary radially symmetric potential which meets certain conditions. Secondly, it is shown that this approach can be generalised to obtain convergence of the eigenvalue with smallest modulus and its universality at the finite inner edge of the eigenvalue support. One interesting aspect of this work is the fact that we can use standard techniques from Hermitian random matrices to obtain the EVS of non-Hermitian random matrices.

No description available.

Cette thèse présente des résultats nouveaux dans deux sujets de la physique statistique du désordre: les modèles aux energies aléatoires logarithmiquement corrélées (logREMs), et la transition de localisation dans les matrices aléatoires à longues portées.Dans la première partie consacrée aux logREMs, nous montrons comment décrire leurs points communs et les données spécifiques aux modèles particuliers. Ensuite nous appliquons la méthode de la brisure de symétrie des répliques pour les étudier en general, et en déduirons la transition vitreuse et le processus des minima, en termes de processus de Poisson décorés. Nous présentons également une série d'application des polynômes de Jack à la prédiction exactes des observables dans le modèle circulaire et ses variants. Finalement, nous décrivons les progrès récents sur la connexion exacte entre les logREMs et la théorie conforme de Liouville.La seconde partie a pour but d'introduire une nouvelle classe de matrices aléatoires à bandes, dite la classe des distributions larges; elle ressemble essentiellement aux matrices creuses. Nous étudions d'abord un modèle particulier de la classe, les matrices aléatoires Bêta, qui sont inspirées par une correspondence exacte à un modèle statistique récemment étudié, celui de la dynamique épidémique. A l'aide des arguments analytiques appuyés sur la correspondence et des simulations numériques, nous montrons l'existence des transitions de localisation avec des valeurs propres critiques dans le régime des paramètres dit d'exponentielle étirée. Ensuite, en utilisant une approche de renormalisation et de diagonalisation par blocs, nous soutenons que les transitions de localisation sont en général présentes dans la class des distributions larges. / This thesis presents original results in two domains of disordered statistical physics: logarithmic correlated Random Energy Models (logREMs), and localization transition in long-range random matrices.In the first part devoted to logREMs, we show how to characterise their common properties and model--specific data. Then we develop their replica symmetry breaking treatment, which leads to the freezing scenario of their free energy distribution and the general description of their minima process, in terms of decorated Poisson point process. We also report a series of new applications of the Jack polynomials in the exact predictions of some observables in the circular model and its variants. Finally, we present the recent progress on the exact connection between logREMs and the Liouville conformal field theory.The goal of the second part is to introduce and study a new class of banded random matrices, the broadly distributed class, which is characterid an effective sparseness. We will first study a specific model of the class, the Beta Banded random matrices, inspired by an exact mapping to a recently studied statistical model of long--range first--passage percolation/epidemics dynamics. Using analytical arguments based on the mapping and numerics, we show the existence of localisation transitions with mobility edges in the ``stretch--exponential'' parameter--regime of the statistical models. Then, using a block--diagonalization renormalization approach, we argue that such localization transitions occur generically in the broadly distributed class.

Cette thèse est consacrée à l'étude des matrices aléatoires et à quelques unes de leurs applications en physique, en particulier en physique statistique et en physique quantique.C'est un travail essentiellement analytique complété par quelques simulations numériques Monte Carlo. Dans un premier temps j'introduis la théorie des matrices aléatoires de façon assez générale : je définis les principaux ensembles de matrices aléatoires (en particulier gaussiens) et décris leurs propriétés fondamentales (distribution des valeurs propres, densité, etc). Dans un second temps je m'intéresse à des systèmes physiques d'interfaces à l'équilibre qui peuvent être modélisés par des marcheurs ``vicieux'', c'est-à-dire des marcheurs aléatoires conditionnés à ne pas se croiser. On peut montrer que la distribution des positions des marcheurs à un temps donné est exactement celle des valeurs propres d'une matrice aléatoire. J'étudie ensuite un problème physique qui relève d'un domaine très différent, celui de l'information quantique, mais qui est également étroitement relié aux matrices aléatoires: celui de l'intrication pour des états aléatoires dans un système quantique bipartite (fait de deux sous-parties) de grande taille. Enfin je m'intéresse à certaines propriétés des matrices aléatoires comme la distribution du nombre de valeurs propres positives ou encore la distribution de la valeur propre maximale (loi de Tracy-Widom près de la moyenne et grandes déviations loin de la moyenne). / This thesis presents a study of random matrices and some applications in physics, in particular in statistical physics and quantum physics. This work is mostly analytic, but I also performed some Monte Carlo numerical simulations. First I introduce random matrix theory: I define the main random matrix ensembles (in particular Gaussian ensembles) and describe their fundamental properties (distribution of the eigenvalues, density...). Then I study a physical system of interfaces at equilibrium that can be modeled by ``vicious walkers'', ie random walkers that can not meet each other.One can show that the distribution of the positions of the walkers at a given time is the same as the distribution of the eigenvalues of a random matrix. I also consider a problem coming from a very different field, the field of quantum information theory, but that is also closely related to random matrices: the distribution of entanglement for random states in a large bipartite quatum system (made of two parts). Finally I study some properties of random matrices such as the distribution of the number of positive eigenvalues or the one of the maximal eigenvalue (Tracy-Widom and large deviations).