Cette thèse s'inscrit dans le cadre général de l'étude de la dynamique de populations. Elle porte sur la modélisation et l'analyse mathématique de l'hématopoïèse, le processus de production et de régulation des cellules sanguines. La population de cellules est perçue comme un milieu continu avec une structuration en âge et en espace. Nous avons commencé par analyser des modèles d'équations différentielles et aux différences à retard discret et distribué. Ces modèles à retard permettent de mettre en évidence des comportements particuliers tels que l'existence de solutions périodiques. Ensuite, nous avons pris en compte l'aspect spatial et la diffusion des cellules dans ces modèles, tout en sachant que la structuration en espace, dans le cas de l'hématopoïèse, a été très peu abordée par le passé. Un nouveau modèle a été obtenu du point de vue mathématique. Une étude d'existence d'ondes progressives est effectuée lorsque le domaine est non borné et lorsque le domaine est borné une étude de stabilité des états stationnaires ainsi que de l'existence d'une bifurcation de Hopf est réalisée / This thesis focuses on the study of population dynamics. It is devoted to the mathematical analysis and modeling of hematopoiesis, which is the process leading to the production and regulation of blood cells. The cell's population is seen as a continuous medium structured in age and space. We analyzed models of differential-difference system with discrete- and distributed -delay. These models can exhibit specific behaviors such as the existence of periodic solutions. Then we consider a space structuration and the diffusion of cells in such models, knowing that the space structure has not been widely studied in the case of hematopoiesis. A new model is obtained from the mathematical point of view. We studied the existence of traveling waves when the domain is unbounded. When the domain is bounded, the stability of stationary solutions and the existence of a Hopf bifurcation are obtained

Nous considérons des processus de diffusion, définis par des équations différentielles stochastiques, et puis nous nous intéressons à des problèmes de premier passage pour les chaînes de Markov en temps discret correspon- dant à ces processus de diffusion. Comme il est connu dans la littérature, ces chaînes convergent en loi vers la solution des équations différentielles stochas- tiques considérées. Notre contribution consiste à trouver des formules expli- cites pour la probabilité de premier passage et la durée de la partie pour ces chaînes de Markov à temps discret. Nous montrons aussi que les résultats ob- tenus convergent selon la métrique euclidienne (i.e topologie euclidienne) vers les quantités correspondantes pour les processus de diffusion. En dernier lieu, nous étudions un problème de commande optimale pour des chaînes de Markov en temps discret. L’objectif est de trouver la valeur qui mi- nimise l’espérance mathématique d’une certaine fonction de coût. Contraire- ment au cas continu, il n’existe pas de formule explicite pour cette valeur op- timale dans le cas discret. Ainsi, nous avons étudié dans cette thèse quelques cas particuliers pour lesquels nous avons trouvé cette valeur optimale. / We consider diffusion processes, defined by stochastic differential equa- tions, and then we focus on first passage problems for Markov chains in dis- crete time that correspond to these diffusion processes. As it is known in the literature, these Markov chains converge in distribution to the solution of the stochastic differential equations considered. Our contribution is to obtain ex- plicit formulas for the first passage probability and the duration of the game for the discrete-time Markov chains. We also show that the results obtained converge in the Euclidean metric to the corresponding quantities for the diffu- sion processes. Finally we study an optimal control problem for Markov chains in discrete time. The objective is to find the value which minimizes the expected value of a certain cost function. Unlike the continuous case, an explicit formula for this optimal value does not exist in the discrete case. Thus we study in this thesis some particular cases for which we found this optimal value.

Cette thèse s'intéresse à la classification géométrique, locale et globale, des équations aux q-différences. Dans un premier temps nous réalisons une étude globale de certains systèmes dérivés des équations de q-Painlevé et introduits par Murata, en proposant une correspondance de Riemann-Hilbert-Birkhoff entre de tels systèmes et leurs matrices de connexion. Dans un second temps nous nous intéressons à la classification locale, en construisant un fibré vectoriel équivariant sur l'espace des classes formelles à deux pentes dont la fibre au dessus d'une classe formelle est l'espace de ses classes analytiques isoformelles. Ceci fait, voyant que l'action du groupe des automorphismes du gradué s'impose naturellement dans l'étude de ce fibré, nous nous intéressons à l'espace des classes analytiques, soit des classes analytiques isoformelles modulo cette action, dont nous proposons dans un cas restreint une première approche de classification via l'utilisation de variétés toriques. Dans un troisième temps nous construisons, via des transformations de q-Borel et de q-Laplace, des q-Stokes, soit des solutions méromorphes de systèmes, dans le cadre des systèmes à deux pentes dont une non entière et une nulle. / This thesis falls within the context of global and local geometric classification of q-difference equations. In a first part we study the global behaviour of some systems derived from q-Painlevé equations and introduced by Murata. We do so by constructing a Riemann-Hilbert-Birkhoff correspondence between such systems and their connexion matrices. In a second part we work on local classification by providing a construction of an equivariant vector bundle over the space of all formal classes with two slopes, the fibre over a formal class being the space of its isoformal analytic classes. As the action of the group of automorphisms of the graded module arises naturally when we study this bundle, we take an interest in the study of the space of analytic classes, which is the space of isoformal analytic classes modulo this action. We propose a first approach of such a classification by using toric varieties. In a third part we construct q-Stokes, i.e. meromorphic solutions of systems, in the context of systems with one non-integral slope and one equal to zero, this by using q-Borel and q-Laplace transforms.

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<div>The orbits of interest for potential missions are stable or nearly stable to maintain long term presence for conducting scientific studies and to reduce the possibility of rapid departure. Near Rectilinear Halo Orbits (NRHOs) offer such stable or nearly stable orbits that are defined as part of the L1 and L2 halo orbit families in the circular restricted three-body problem. Within the Earth-Moon regime, the L1 and L2 NRHOs are proposed as long horizon trajectories for cislunar exploration missions, including NASA's upcoming Gateway mission. These stable or nearly stable orbits do not possess well-distinguished unstable and stable manifold structures. As a consequence, existing tools for stationkeeping and transfer trajectory design that exploit such underlying manifold structures are not reliable for orbits that are linearly stable. The current investigation focuses on leveraging stretching direction as an alternative for visualizing the flow of perturbations in the neighborhood of a reference trajectory. The information supplemented by the stretching directions are utilized to investigate the impact of maneuvers for two contrasting applications; the stationkeeping problem, where the goal is to maintain a spacecraft near a reference trajectory for a long period of time, and the transfer trajectory design application, where rapid departure and/or insertion is of concern.</div><div><br></div><div>Particularly, for the stationkeeping problem, a spacecraft incurs continuous deviations due to unmodeled forces and orbit determination errors in the complex multi-body dynamical regime. The flow dynamics in the region, using stretching directions, are utilized to identify appropriate maneuver and target locations to support a long lasting presence for the spacecraft near the desired path. The investigation reflects the impact of various factors on maneuver cost and boundedness. For orbits that are particularly sensitive to epoch time and possess distinct characteristics in the higher-fidelity ephemeris model compared to their CR3BP counterpart, an additional feedback control is applied for appropriate phasing. The effect of constraining maneuvers in a particular direction is also investigated for the 9:2 synodic resonant southern L2 NRHO, the current baseline for the Gateway mission. The stationkeeping strategy is applied to a range of L1 and L2 NRHOs, and validated in the higher-fidelity ephemeris model.</div><div><br></div><div>For missions with potential human presence, a rapid transfer between orbits of interest is a priority. The magnitude of the state variations along the maximum stretching direction is expected to grow rapidly and, therefore, offers information to depart from the orbit. Similarly, the maximum stretching in reverse time, enables arrival with a minimal maneuver magnitude. The impact of maneuvers in such sensitive directions is investigated. Further, enabling transfer design options to connect between two stable orbits. The transfer design strategy developed in this investigation is not restricted to a particular orbit but applicable to a broad range of stable and nearly stable orbits in the cislunar space, including the Distant Retrograde Orbit (DROs) and the Low Lunar Orbits (LLO) that are considered for potential missions. Examples for transfers linking a southern and a northern NRHO, a southern NRHO to a planar DRO, and a southern NRHO to a planar LLO are demonstrated.</div>

<p dir="ltr">Neural operator learning is emerging as a prominent technique in scientific machine learn- ing for modeling complex nonlinear systems with multi-physics and multi-scale applications. A common drawback of such operators is that they are data-hungry and the results are highly dependent on the quality and quantity of the training data provided to the models. Moreover, obtaining high-quality data in sufficient quantity can be computationally prohibitive. Faster surrogate models are required to overcome this drawback which can be learned from datasets of variable fidelity and also quantify the uncertainty. In this work, we propose a Multi-Level Stacked Deep Operator Network (MLSDON) which can learn from datasets of different fidelity and is not dependent on the input function. Through various experiments, we demonstrate that the MLSDON can approximate the high-fidelity solution operator with better accuracy compared to a Vanilla DeepONet when sufficient high-fidelity data is unavailable. We also extend MLSDON to build robust confidence intervals by making conformalized predictions. This technique guarantees trajectory coverage of the predictions irrespective of the input distribution. Various numerical experiments are conducted to demonstrate the applicability of MLSDON to multi-fidelity, multi-scale, and multi-physics problems.</p>

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