Ce travail de recherche propose une solution aux problèmes inverses probabilistes avec des outils de la statistique bayésienne. Le problème inverse considéré est d'estimer la distribution d'une variable aléatoire non observée X à partir d'observations bruitées Y suivant un modèle physique coûteux H. En général, de tels problèmes inverses sont rencontrés dans le traitement des incertitudes. Le cadre bayésien nous permet de prendre en compte les connaissances préalables d'experts en particulier lorsque peu de données sont disponibles. Un algorithme de Metropolis-Hastings-within-Gibbs est proposé pour approcher la distribution a posteriori des paramètres de X avec un processus d'augmentation des données. A cause d'un nombre élevé d'appels, la fonction coûteuse H est remplacée par un émulateur de krigeage (métamodèle). Cette approche implique plusieurs erreurs de natures différentes et, dans ce travail,nous nous attachons à estimer et réduire l'impact de ces erreurs. Le critère DAC a été proposé pour évaluer la pertinence du plan d'expérience (design) et le choix de la loi apriori, en tenant compte des observations. Une autre contribution est la construction du design adaptatif adapté à notre objectif particulier dans le cadre bayésien. La méthodologie principale présentée dans ce travail a été appliquée à un cas d'étude en ingénierie hydraulique. / This thesis provides a probabilistic solution to inverse problems through Bayesian techniques.The inverse problem considered here is to estimate the distribution of a non-observed random variable X from some noisy observed data Y explained by a time-consuming physical model H. In general, such inverse problems are encountered when treating uncertainty in industrial applications. Bayesian inference is favored as it accounts for prior expert knowledge on Xin a small sample size setting. A Metropolis-Hastings-within-Gibbs algorithm is proposed to compute the posterior distribution of the parameters of X through a data augmentation process. Since it requires a high number of calls to the expensive function H, the modelis replaced by a kriging meta-model. This approach involves several errors of different natures and we focus on measuring and reducing the possible impact of those errors. A DAC criterion has been proposed to assess the relevance of the numerical design of experiments and the prior assumption, taking into account the observed data. Another contribution is the construction of adaptive designs of experiments adapted to our particular purpose in the Bayesian framework. The main methodology presented in this thesis has been applied to areal hydraulic engineering case-study.

A prevalent morphology in the microscopic world of artificial microswimmers, bacteria and viruses is that of a helix. The intriguingly different physics at play at the small scale level make it necessary for bacteria to employ swimming strategies different from our everyday experience, such as the rotation of a helical filament. Bio-inspired microswimmers that mimic bacterial locomotion achieve propulsion at the microscale level using magnetically actuated, rotating helical filaments. A promising application of these artificial microswimmers is in non-invasive medicine, for drug delivery to tumours or microsurgery. Two crucial features need to be addressed in the design of microswimmers. First, the ability to selectively control large ensembles and second, the adaptivity to move through complex conduit geometries, such as the constrictions and curves of the tortuous tumour microvasculature. In this dissertation, a mechanics-based selective control mechanism for magnetic microswimmers is proposed, and a model and simulation of an elastic helix passing through a constricted microchannel are developed. Thereafter, a theoretical framework is developed for the propulsion by stiff elastic filaments in viscous fluids. In order to address this fluid-structure problem, a pertubative, asymptotic, elastohydrodynamic approach is used to characterise the deformation that arises from and in turn affects the motion. This framework is applied to the helical filaments of bacteria and magnetically actuated microswimmers. The dissertation then turns to the sub-bacterial scale of bacteriophage viruses, 'phages' for short, that infect bacteria by ejecting their genetic material and replicating inside their host. The valuable insight that phages can offer in our fight against pathogenic bacteria and the possibility of phage therapy as an alternative to antibiotics, are of paramount importance to tackle antibiotics resistance. In contrast to typical phages, flagellotropic phages first attach to bacterial flagella, and have the striking ability to reach the cell body for infection, despite their lack of independent motion. The last part of the dissertation develops the first theoretical model for the nut-and-bolt mechanism (proposed by Berg and Anderson in 1973). A nut being rotated will move along a bolt. Similarly, a phage wraps itself around a flagellum possessing helical grooves, and exploits the rotation of the flagellum in order to passively travel along and towards the cell body, according to this mechanism. The predictions from the model agree with experimental observations with respect to directionality, speed and the requirements for succesful translocation.