O modelo de Ising dipolar bidimensional inclui, além da interação ferromagnética entre os primeiros vizinhos, interações de longo alcance entre os momentos de dipolo magnético dos spins. A presença da interação dipolar muda completamente o sistema, apresentando um rico diagrama de fase, cujas características têm originado inúmeros estudos na literatura. Além disso, a possibilidade de explicar fenômenos observados em filmes magnéticos ultrafinos, os quais possuem diversas aplicações em àreas tecnológicas, também motiva o estudo deste modelo. O estado fundamental ferromagnético do modelo de Ising puro é alterado para uma série de fases do tipo faixas, as quais consistem em domínios ferromagnéticos de largura $h$ com magnetizações opostas. A largura das faixas depende da razao $\\delta$ das intensidades dos acoplamentos ferromagnético e dipolar. Através de simulações de Monte Carlo e técnicas de repesagem em histogramas múltiplos identificamos as temperaturas críticas de tamanho finito para as transições de fase quando $\\delta=2$, o que corresponde a $h=2$. Calculamos o calor específico e a susceptibilidade do parâmetro de ordem, no intervalo de temperaturas onde as transições são observadas, para diferentes tamanhos de rede. As técnicas de repesagem permitem-nos explorar e identificar máximos distintos nessas funções da temperatura e, desse modo, estimar as temperaturas críticas de tamanho finito com grande precisão. Apresentamos evidências numéricas da existência de uma fase nemática de Ising para tamanhos grandes de rede. Em nossas simulações, observamos esta fase para tamanhos de rede a partir de $L=48$. Para verificar o quanto a interação dipolar de longo alcance afeta as estimativas físicas, nós calculamos o tempo de autocorrelação integrado nas séries temporais da energia. Inferimos daí quão severo é o critical slowing down (decaimento lento crítico) para esse sistema próximo às transições de fase termodinâmicas. Os resultados obtidos utilizando um algoritmo de atualização local foram comparados com os resultados obtidos utilizando o algoritmo multicanônico. / Two-dimensional spin model with nearest-neighbor ferromagnetic interaction and long-range dipolar interactions exhibit a rich phase diagram, whose characteristics have been exploited by several studies in the recent literature. Furthermore, the possibility of explain observed phenomena in ultrathin magnetic films, which have many technological applications, also motivates the study of this model. The presence of dipolar interaction term changes the ferromagnetic ground state expected for the pure Ising model to a series of striped phases, which consist of ferromagnetic domains of width $h$ with opposite magnetization. The width of the stripes depends on the ratio $\\delta$ of the ferromagnetic and dipolar couplings. Monte Carlo simulations and reweighting multiple histograms techniques allow us to identify the finite-size critical temperatures of the phase transitions when $\\delta=2$, which corresponds to $h=2$. We calculate, for different lattice sizes, the specific heat and susceptibility of the order parameter around the transition temperatures by means of reweighting techniques. This allows us to identify in these observables, as functions of temperature, the distinct maxima and thereby to estimate the finite-size critical temperatures with high precision. We present numerical evidence of the existence of a Ising nematic phase for large lattice sizes. Our results show that simulations need to be performed for lattice sizes at least as large as $L=48$ to clearly observe the Ising nematic phase. To access how the long-range dipolar interaction may affect physical estimates we also evaluate the integrated autocorrelation time in energy time series. This allows us to infer how severe is the critical slowing down for this system with long-range interaction and nearby thermodynamic phase transitions. The results obtained using a local update algorithm are compared with results obtained using the multicanonical algorithm.

Nous présentons dans ce mémoire un nouvel algorithme de type Metropolis-Hastings dans lequel la distribution instrumentale a été conçue pour l'estimation de distributions cibles bimodales. En fait, cet algorithme peut être vu comme une modification de l'algorithme Metropolis de type marche aléatoire habituel auquel on ajoute quelques incréments de grande envergure à des moments aléatoires à travers la simulation. Le but de ces grands incréments est de quitter le mode de la distribution cible où l'on se trouve et de trouver l'autre mode. Par la suite, nous présentons puis démontrons un résultat de convergence faible qui nous assure que, lorsque la dimension de la distribution cible croît vers l'infini, la chaîne de Markov engendrée par l'algorithme converge vers un certain processus stochastique qui est continu presque partout. L'idée est similaire à ce qui a été fait par Roberts et al. (1997), mais la technique utilisée pour la démonstration des résultats est basée sur ce qui a été fait par Bédard (2006). Nous proposons enfin une stratégie pour trouver la paramétrisation optimale de notre nouvel algorithme afin de maximiser la vitesse d'exploration locale des modes d'une distribution cible donnée tout en estimant bien la pondération relative de chaque mode. Tel que dans l'approche traditionnellement utilisée pour ce genre d'analyse, notre stratégie passe par l'optimisation de la vitesse d'exploration du processus limite. Finalement, nous présentons des exemples numériques d'implémentation de l'algorithme sur certaines distributions cibles, dont une ne respecte pas les conditions du résultat théorique présenté. / In this thesis, we present a new Metropolis-Hastings algorithm whose proposal distribution has been designed to successfully estimate bimodal target distributions. This sampler may be seen as a variant of the usual random walk Metropolis sampler in which we propose large candidate steps at random times. The goal of these large candidate steps is to leave the actual mode of the target distribution in order to find the second one. We then state and prove a weak convergence result stipulating that if we let the dimension of the target distribution increase to infinity, the Markov chain yielded by the algorithm converges to a certain stochastic process that is almost everywhere continuous. The theoretical result is in the flavour of Roberts et al. (1997), while the method of proof is similar to that found in Bédard (2006). We propose a strategy for optimally parameterizing our new sampler. This strategy aims at optimizing local exploration of the target modes, while correctly estimating the relative weight of each mode. As is traditionally done in the statistical literature, our approach consists of optimizing the limiting process rather than the finite-dimensional Markov chain. Finally, we illustrate our method via numerical examples on some target distributions, one of which violates the regularity conditions of the theoretical result.

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L’objectif de cette thèse est l’étude d’un schéma numérique pour l’approximation des solutions d’équations différentielles doublement stochastiques rétrogrades (EDDSR). Durant les deux dernières décennies, plusieurs méthodes ont été proposées afin de permettre la résolution numérique des équations différentielles stochastiques rétrogrades standards. Dans cette thèse, on propose une extension de l’une de ces méthodes au cas doublement stochastique. Notre méthode numérique nous permet d’attaquer une large gamme d’équations aux dérivées partielles stochastiques (EDPS) nonlinéaires. Ceci est possible par le biais de leur représentation probabiliste en termes d’EDDSRs. Dans la dernière partie, nous étudions une nouvelle méthode des particules dans le cadre des études de protection en neutroniques. / The purpose of this thesis is to study a numerical method for backward doubly stochastic differential equations (BDSDEs in short). In the last two decades, several methods were proposed to approximate solutions of standard backward stochastic differential equations. In this thesis, we propose an extension of one of these methods to the doubly stochastic framework. Our numerical method allows us to tackle a large class of nonlinear stochastic partial differential equations (SPDEs in short), thanks to their probabilistic interpretation. In the last part, we study a new particle method in the context of shielding studies.