The Schrödinger equations have had a profound impact on a wide range of fields of modern science, including quantum mechanics, superfluidity, geometrical optics, Bose-Einstein condensates, and the analysis of dispersive phenomena in the theory of PDE. The main purpose of this thesis is to explore two Schrödinger-type equations appearing in the so-called Bohmian formulation of quantum mechanics and in the study of exciton-polariton condensates. For the first topic, the linear Schrödinger equation is the starting point in the formulation of a phase-space model proposed in [1] for the Bohmian interpretation of quantum mechanics. We analyze this model, a nonlinear Vlasov-type equation, as a Hamiltonian system defined on an appropriate Poisson manifold built on Wasserstein spaces, the aim being to establish its existence theory. For this purpose, we employ results from the theory of PDE, optimal transportation, differential geometry and algebraic topology. The second topic of the thesis is the study of a nonlinear Schrödinger equation, called the complex Gross-Pitaevskii equation, appearing in the context of Bose-Einstein condensation of exciton-polaritons. This model can be roughly described as a driven-damped Gross-Pitaevskii equation which shares some similarities with the complex Ginzburg-Landau equation. The difficulties in the analysis of this equation stem from the fact that, unlike the complex Ginzburg-Landau equation, the complex Gross-Pitaevskii equation does not include a viscous dissipation term. Our approach to this equation will be in the framework of numerical computations, using two main tools: collocation methods and numerical continuation for the stationary solutions and a time-splitting spectral method for the dynamics. After performing a linear stability analysis on the computed stationary solutions, we are led to postulate the existence of radially symmetric stationary ground state solutions only for certain values of the parameters in the equation; these parameters represent the “strength” of the driving and damping terms. Moreover, numerical continuation allows us to show, for fixed parameters, the ground and some of the excited state solutions of this equation. Finally, for the values of the parameters that do not produce a stable radially symmetric solution, our dynamical computations show the emergence of rotating vortex lattices.

Cette thèse contient deux sujets différents la simulation d'événements rares et un transport d'homotopie pour l'estimation du modèle de volatilité stochastique, dont chacun est couvert dans une partie distincte de cette thèse. Les méthodes de particules, qui généralisent les modèles de Markov cachés, sont largement utilisées dans différents domaines tels que le traitement du signal, la biologie, l'estimation d'événements rares, la finance, etc. Il existe un certain nombre d'approches basées sur les méthodes de Monte Carlo, tels que Markov Chain Monte Carlo (MCMC), Monte Carlo séquentiel (SMC). Nous appliquons des algorithmes SMC pour estimer les probabilités de défaut dans un processus d'intensité basé sur un processus stable afin de calculer un ajustement de valeur de crédit (CV A) avec le wrong way risk (WWR). Nous proposons une nouvelle approche pour estimer les événements rares, basée sur la génération de chaînes de Markov en simulant le système hamiltonien. Nous démontrons les propriétés, ce qui nous permet d'avoir une chaîne de Markov ergodique et nous montrons la performance de notre approche sur l'exemple que nous rencontrons dans la valorisation des options. Dans la deuxième partie, nous visons à estimer numériquement un modèle de volatilité stochastique, et à le considérer dans le contexte d'un problème de transport, lorsque nous aimerions trouver «un plan de transport optimal» qui représente la mesure d'image. Dans un contexte de filtrage, nous le comprenons comme le transport de particules d'une distribution antérieure à une distribution postérieure dans le pseudo-temps. Nous avons également proposé de repondérer les particules transportées, de manière à ce que nous puissions nous diriger vers la zone où se concentrent les particules de poids élevé. Nous avons montré sur l'exemple du modèle de la volatilité stochastique de Stein-Stein l'application de notre méthode et illustré le biais et la variance. / The thesis introduces simulation techniques that are based on particle methods and it consists of two parts, namely rare event simulation and a homotopy transport for stochastic volatility model estimation. Particle methods, that generalize hidden Markov models, are widely used in different fields such as signal processing, biology, rare events estimation, finance, etc. There are a number of approaches that are based on Monte Carlo methods that allow to approximate a target density such as Markov Chain Monte Carlo (MCMC), sequential Monte Carlo (SMC). We apply SMC algorithms to estimate default probabilities in a stable process based intensity process to compute a credit value adjustment (CV A) with a wrong way risk (WWR). We propose a novel approach to estimate rare events, which is based on the generation of Markov Chains by simulating the Hamiltonian system. We demonstrate the properties, that allows us to have ergodic Markov Chain and show the performance of our approach on the example that we encounter in option pricing.In the second part, we aim at numerically estimating a stochastic volatility model, and consider it in the context of a transportation problem, when we would like to find "an optimal transport map" that pushes forward the measure. In a filtering context, we understand it as the transportation of particles from a prior to a posterior distribution in pseudotime. We also proposed to reweight transported particles, so as we can direct to the area, where particles with high weights are concentrated. We showed the application of our method on the example of option pricing with Stein­Stein stochastic volatility model and illustrated the bias and variance.