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Le but de cette thèse est d’étudier, à l’aide d’outils combinatoires simples, différentes structures géométriques construites à partir de l’action d’un polynôme ou d’une fraction rationnelle. Nous considérerons d’abord la structure de l'ensemble des solutions séparatrices d’un champ de vecteurs polynomial ou rationnel. Nous allons établir plusieurs modèles combinatoires de ces cartes planaires, ainsi qu’une formule fermée énumérant les différentes structures topologiques dans le cas polynomial. Puis nous parlerons de revêtements ramifiés de la sphère que nous modéliserons, via un objet combinatoire nommée carte équilibrée, à partir d’une idée originale de W.Thurston. Ce modèle nous permettra de démontrer (géométriquement) de nombreuses propriétés de ces objets, et d’offrir une nouvelle approche et de nouvelles perspectives au problème d’Hurwitz, qui reste encore aujourd’hui un problème ouvert. Et enfin nous aborderons le sujet de la dynamique holomorphe via les primitives majeures dont l’utilité est de permettre de paramétrer les systèmes dynamiques engendrés par l’itération de polynômes. Cette approche nous permettra de construire une bijection entre les suites de parking et les arbres de Cayley, ainsi que d’établir une formule fermée liée à l’énumération d’un certain type d’arbres relié à la fois aux primitives majeures et aux revêtements ramifiés polynomiaux. / The main topic of this thesis is to study, thanks to simple combinatorial tools, various geometric structures coming from the action of a complex polynomial or a rational function on the sphere. The first structure concerns separatrix solutions of polynomial or rational vector fields. We will establish several combinatorial models of these planar maps, as well as a closed formula enumerating the different topological structures that arise in the polynomial settings. Then, we will focus on branched coverings of the sphere. We establish a combinatorial coding of these mappings using the concept of balanced maps, following an original idea of W. Thurston. This combinatorics allows us to prove (geometrically) several properties about branched coverings, and gives us a new approach and perspective to address the still open Hurwitz problem. Finally, we discuss a dynamical problem represented by primitive majors. The utility of these objects is to allow us to parameterize dynamical systems generated by the iterations of polynomials. This approach will enable us to construct a bijection between parking functions and Cayley trees, and to establish a closed formula enumerating a certain type of trees related to both primitive majors and polynomial branched coverings.

[pt] Pontes com vãos superiores a 2.000 m tornam-se muito sensíveis à ação do vento, particularmente ao drapejamento. Nesta tese é estudado um método para a supressão do drapejamento em pontes de grandes vãos através de um controle aerodinâmico ativo. Apresentam-se técnicas analíticas de projeto para o controle ativo do sistema aero elástico constituído pelo tabuleiro e por duas superfícies de controle. Estas técnicas são baseadas em aproximações racionais das cargas aerodinâmicas não permanentes (ou auto-excitadas) no domínio Laplaciano, no qual as equações de movimento são representadas por equações matriciais de coeficientes constantes. A primeira parte da tese é dedicada à formulação matricial das funções racionais conhecida como Minimum State, assim como a aplicações a dados aerodinâmicos obtidos experimentalmente para vários tipos de seções transversais de pontes. A precisão das aproximações é calculada. Desenhos dos derivativos aerodinâmicos, dados sob forma de tabelas, e das respectivas aproximações, são elaborados para fins de comparação. Em seguida, são apresentadas as equações em espaço de estado descrevendo o comportamento aeroelástico de uma seção transversal de ponte. A partir dos dados geométricos e características dinâmicas de uma determinada ponte, (massa, momento de inertia polar, frequências naturais e fatores de amortecimento), e assumindo a semelhança geométrica entre as seções transversais da ponte em verdadeira grandeza e do modelo em escala do qual os derivativos aerodinâmicos foram extraídos, é possível calcular a velocidade crítica desta ponte, utilizando os programas em linguagem MATLAB apresentados no corpo deste trabalho. Esta parte da tese mostra ser possível construir um catálogo com vários perfis de pontes, caracterizados por derivativos aerodinâmicos variáveis em função de frequências reduzidas adimensionais, e das funções racionais correspondentes. A segunda parte é dedicada à fomulação das equações de movimento em espaço de estado, descrevendo o comportamento aeroelástico do sistema tabuleiro - superfícies de controle. As equações resultantes são ampliadas com novos estados aerodinâmicos responsáveis pela modelagem da influência do fluxo de ar sobre o tabuleiro e sobre as superfícies de controle em movimento. As equações de movimento são função da velocidade média do vento incidente. A dependência da equação de movimento à velocidade do vento motivou a aplicação dos conceitos de realimentação de ganhos, constante e variável, ao problema da supressão do drapejamento, os quais são apresentados separadamente em dois capítulos.O enfoque de ganho variável de saída é formulado em termos de minimização de um índice de desempenho dimensionalmente proporcional à soma do trabalho realizado pelas superfícies de controle e da energia cinética proporcional à velocidade vertical do tabuleiro. Apresenta-se também em detalhe um método sistemático para determinar a matriz de controle de ganhos variável, aplicada ao caso hipotético da ponte de Gibraltar. Neste caso, o conceito de realimentação de ganhos variável mostrou-se muito efetivo em suprimir o drapejamento do tabuleiro da ponte. Diferentes características geométricas e dinâmicas de outras pontes podem ser introduzidas nos programas MATLAB apresentados no Apêndice, para obtenção da velocidade crítica nos casos de tabuleiros isolados, tabuleiros com asas estacionárias e tabuleiros com asas giratórias ativamente controladas, para supressão do drapejamento do tabuleiro. / [en] Long span bridges, with main spans beyond 2.000 m become highly sensitive to wind action, particularly to flutter. An active aerodynamic control method of suppressing flutter of very long span bridges is studied in this thesis. Analytical design techniques for active control of the aeroelastic system consisting of the bridge deck and two control surfaces are presented. These techniques are based on a rational approximation of the unsteady aerodynamic loads in the entire Laplace domain, which yieds matrix equations of motion with constant coefficientes. The first part of this thesis is dedicated to the matrix formulation of the rational functions known as Minimum State and to applications to aerodynamic data obtained experimentally for various types of bridge profiles. The precision of the approximations iscalculated, and plots of the approximation functions compared to the available tabular data are drawn. Next, the state-space equations of motion describing the aeroelastic behaviour of a section of a bridge deck is presented. Given the dynamic data of a bridge structure (mass, rotational mass moment of inertia, natural frequencies, stiffness and damping ratios), and assuming that a geometric similitude exists between the profiles of the full-scale bridge deck and the sectional model from which the frequency dependent aerodynamic data was extracted, it is possible to calculate the critical velocity of that particular bridge. This part of the thesis shows that it is possible to build up a catalog of several profiles, characterized by frequency dependent aerodynamic data and the corresponding rational functions. The second part is dedicated to the formulation of the state-space equations of motion describing the aeroelastic behaviour of the entire system consisting of the bridge deck and control surfaces. The resulting equation includes new aerodynamic states which model the air flow influence on the moving deck. The equation of motion is a function of the mean velocity of the incoming wind. The dependence of the equation of motion on the wind velocity motivated the application of a constant and a variable-gain feedback concept to the problem of flutter suppressing, which are presented separatelly. The output variable-gain approach is formulated in terms of minimizing a performance index dimensionally proportional to the sum of the work done by the rotating control surfaces and the kinetic energy of the heaving velocity. A sistematic method to determine the matrix of variable control gains is shown in detail, as applied to the hypothethical case of Gibraltar bridge. Application of the variablegain feedback concept was found to be very effective in suppressing flutter of the bridge deck. Different geometric and dynamic characteristics can be introduced in the MATLAB programs included in this work, in order to obtain the critical velocities of a bridge deck alone, a bridge deck with stationary wings and a bridge with moving wings activelly controled.

Cette thèse cherche à examiner les conditions optimales dans lesquelles les surfaces cornéennes antérieures peuvent être efficacement pré-traitées, classifiées et prédites en utilisant des techniques de modélisation géométriques (MG) et d’apprentissage automatiques (AU). La première étude (Chapitre 2) examine les conditions dans lesquelles la modélisation géométrique peut être utilisée pour réduire la dimensionnalité des données utilisées dans un projet d’apprentissage automatique. Quatre modèles géométriques ont été testés pour leur précision et leur rapidité de traitement : deux modèles polynomiaux (P) – polynômes de Zernike (PZ) et harmoniques sphériques (PHS) – et deux modèles de fonctions rationnelles (R) : fonctions rationnelles de Zernike (RZ) et fonctions rationnelles d’harmoniques sphériques (RSH). Il est connu que les modèles PHS et RZ sont plus précis que les modèles PZ pour un même nombre de coefficients (J), mais on ignore si les modèles PHS performent mieux que les modèles RZ, et si, de manière plus générale, les modèles SH sont plus précis que les modèles R, ou l’inverse. Et prenant en compte leur temps de traitement, est-ce que les modèles les plus précis demeurent les plus avantageux? Considérant des valeurs de J (nombre de coefficients du modèle) relativement basses pour respecter les contraintes de dimensionnalité propres aux taches d’apprentissage automatique, nous avons établi que les modèles HS (PHS et RHS) étaient tous deux plus précis que les modèles Z correspondants (PZ et RR), et que l’avantage de précision conféré par les modèles HS était plus important que celui octroyé par les modèles R. Par ailleurs, les courbes de temps de traitement en fonction de J démontrent qu’alors que les modèles P sont traités en temps quasi-linéaires, les modèles R le sont en temps polynomiaux. Ainsi, le modèle SHR est le plus précis, mais aussi le plus lent (un problème qui peut en partie être remédié en appliquant une procédure de pré-optimisation). Le modèle ZP était de loin le plus rapide, et il demeure une option intéressante pour le développement de projets. SHP constitue le meilleur compromis entre la précision et la rapidité. La classification des cornées selon des paramètres cliniques a une longue tradition, mais la visualisation des effets moyens de ces paramètres sur la forme de la cornée par des cartes topographiques est plus récente. Dans la seconde étude (Chapitre 3), nous avons construit un atlas de cartes d’élévations moyennes pour différentes variables cliniques qui pourrait s’avérer utile pour l’évaluation et l’interprétation des données d’entrée (bases de données) et de sortie (prédictions, clusters, etc.) dans des tâches d’apprentissage automatique, entre autres. Une base de données constituée de plusieurs milliers de surfaces cornéennes antérieures normales enregistrées sous forme de matrices d’élévation de 101 by 101 points a d’abord été traitée par modélisation géométrique pour réduire sa dimensionnalité à un nombre de coefficients optimal dans une optique d’apprentissage automatique. Les surfaces ainsi modélisées ont été regroupées en fonction de variables cliniques de forme, de réfraction et de démographie. Puis, pour chaque groupe de chaque variable clinique, une surface moyenne a été calculée et représentée sous forme de carte d’élévations faisant référence à sa SMA (sphère la mieux ajustée). Après avoir validé la conformité de la base de donnée avec la littérature par des tests statistiques (ANOVA), l’atlas a été vérifié cliniquement en examinant si les transformations de formes cornéennes présentées dans les cartes pour chaque variable étaient conformes à la littérature. C’était le cas. Les applications possibles d’un tel atlas sont discutées. La troisième étude (Chapitre 4) traite de la classification non-supervisée (clustering) de surfaces cornéennes antérieures normales. Le clustering cornéen un domaine récent en ophtalmologie. La plupart des études font appel aux techniques d’extraction des caractéristiques pour réduire la dimensionnalité de la base de données cornéennes. Le but est généralement d’automatiser le processus de diagnostique cornéen, en particulier en ce qui a trait à la distinction entre les cornées normales et les cornées irrégulières (kératocones, Fuch, etc.), et dans certains cas, de distinguer différentes sous-classes de cornées irrégulières. L’étude de clustering proposée ici se concentre plutôt sur les cornées normales afin de mettre en relief leurs regroupements naturels. Elle a recours à la modélisation géométrique pour réduire la dimensionnalité de la base de données, utilisant des polynômes de Zernike, connus pour leur interprétativité transparente (chaque terme polynomial est associé à une caractéristique cornéenne particulière) et leur bonne précision pour les cornées normales. Des méthodes de différents types ont été testées lors de prétests (méthodes de clustering dur (hard) ou souple (soft), linéaires or non-linéaires. Ces méthodes ont été testées sur des surfaces modélisées naturelles (non-normalisées) ou normalisées avec ou sans traitement d’extraction de traits, à l’aide de différents outils d’évaluation (scores de séparabilité et d’homogénéité, représentations par cluster des coefficients de modélisation et des surfaces modélisées, comparaisons statistiques des clusters sur différents paramètres cliniques). Les résultats obtenus par la meilleure méthode identifiée, k-means sans extraction de traits, montrent que les clusters produits à partir de surfaces cornéennes naturelles se distinguent essentiellement en fonction de la courbure de la cornée, alors que ceux produits à partir de surfaces normalisées se distinguent en fonction de l’axe cornéen. La dernière étude présentée dans cette thèse (Chapitre 5) explore différentes techniques d’apprentissage automatique pour prédire la forme de la cornée à partir de données cliniques. La base de données cornéennes a d’abord été traitée par modélisation géométrique (polynômes de Zernike) pour réduire sa dimensionnalité à de courts vecteurs de 12 à 20 coefficients, une fourchette de valeurs potentiellement optimales pour effectuer de bonnes prédictions selon des prétests. Différentes méthodes de régression non-linéaires, tirées de la bibliothèque scikit-learn, ont été testées, incluant gradient boosting, Gaussian process, kernel ridge, random forest, k-nearest neighbors, bagging, et multi-layer perceptron. Les prédicteurs proviennent des variables cliniques disponibles dans la base de données, incluant des variables géométriques (diamètre horizontal de la cornée, profondeur de la chambre cornéenne, côté de l’œil), des variables de réfraction (cylindre, sphère et axe) et des variables démographiques (âge, genre). Un test de régression a été effectué pour chaque modèle de régression, défini comme la sélection d’une des 256 combinaisons possibles de variables cliniques (les prédicteurs), d’une méthode de régression, et d’un vecteur de coefficients de Zernike d’une certaine taille (entre 12 et 20 coefficients, les cibles). Tous les modèles de régression testés ont été évalués à l’aide de score de RMSE établissant la distance entre les surfaces cornéennes prédites (les prédictions) et vraies (les topographies corn¬éennes brutes). Les meilleurs d’entre eux ont été validés sur l’ensemble de données randomisé 20 fois pour déterminer avec plus de précision lequel d’entre eux est le plus performant. Il s’agit de gradient boosting utilisant toutes les variables cliniques comme prédicteurs et 16 coefficients de Zernike comme cibles. Les prédictions de ce modèle ont été évaluées qualitativement à l’aide d’un atlas de cartes d’élévations moyennes élaborées à partir des variables cliniques ayant servi de prédicteurs, qui permet de visualiser les transformations moyennes d’en groupe à l’autre pour chaque variables. Cet atlas a permis d’établir que les cornées prédites moyennes sont remarquablement similaires aux vraies cornées moyennes pour toutes les variables cliniques à l’étude. / This thesis aims to investigate the best conditions in which the anterior corneal surface of normal corneas can be preprocessed, classified and predicted using geometric modeling (GM) and machine learning (ML) techniques. The focus is on the anterior corneal surface, which is the main responsible of the refractive power of the cornea. Dealing with preprocessing, the first study (Chapter 2) examines the conditions in which GM can best be applied to reduce the dimensionality of a dataset of corneal surfaces to be used in ML projects. Four types of geometric models of corneal shape were tested regarding their accuracy and processing time: two polynomial (P) models – Zernike polynomial (ZP) and spherical harmonic polynomial (SHP) models – and two corresponding rational function (R) models – Zernike rational function (ZR) and spherical harmonic rational function (SHR) models. SHP and ZR are both known to be more accurate than ZP as corneal shape models for the same number of coefficients, but which type of model is the most accurate between SHP and ZR? And is an SHR model, which is both an SH model and an R model, even more accurate? Also, does modeling accuracy comes at the cost of the processing time, an important issue for testing large datasets as required in ML projects? Focusing on low J values (number of model coefficients) to address these issues in consideration of dimensionality constraints that apply in ML tasks, it was found, based on a number of evaluation tools, that SH models were both more accurate than their Z counterparts, that R models were both more accurate than their P counterparts and that the SH advantage was more important than the R advantage. Processing time curves as a function of J showed that P models were processed in quasilinear time, R models in polynomial time, and that Z models were fastest than SH models. Therefore, while SHR was the most accurate geometric model, it was the slowest (a problem that can partly be remedied by applying a preoptimization procedure). ZP was the fastest model, and with normal corneas, it remains an interesting option for testing and development, especially for clustering tasks due to its transparent interpretability. The best compromise between accuracy and speed for ML preprocessing is SHP. The classification of corneal shapes with clinical parameters has a long tradition, but the visualization of their effects on the corneal shape with group maps (average elevation maps, standard deviation maps, average difference maps, etc.) is relatively recent. In the second study (Chapter 3), we constructed an atlas of average elevation maps for different clinical variables (including geometric, refraction and demographic variables) that can be instrumental in the evaluation of ML task inputs (datasets) and outputs (predictions, clusters, etc.). A large dataset of normal adult anterior corneal surface topographies recorded in the form of 101×101 elevation matrices was first preprocessed by geometric modeling to reduce the dimensionality of the dataset to a small number of Zernike coefficients found to be optimal for ML tasks. The modeled corneal surfaces of the dataset were then grouped in accordance with the clinical variables available in the dataset transformed into categorical variables. An average elevation map was constructed for each group of corneal surfaces of each clinical variable in their natural (non-normalized) state and in their normalized state by averaging their modeling coefficients to get an average surface and by representing this average surface in reference to the best-fit sphere in a topographic elevation map. To validate the atlas thus constructed in both its natural and normalized modalities, ANOVA tests were conducted for each clinical variable of the dataset to verify their statistical consistency with the literature before verifying whether the corneal shape transformations displayed in the maps were themselves visually consistent. This was the case. The possible uses of such an atlas are discussed. The third study (Chapter 4) is concerned with the use of a dataset of geometrically modeled corneal surfaces in an ML task of clustering. The unsupervised classification of corneal surfaces is recent in ophthalmology. Most of the few existing studies on corneal clustering resort to feature extraction (as opposed to geometric modeling) to achieve the dimensionality reduction of the dataset. The goal is usually to automate the process of corneal diagnosis, for instance by distinguishing irregular corneal surfaces (keratoconus, Fuch, etc.) from normal surfaces and, in some cases, by classifying irregular surfaces into subtypes. Complementary to these corneal clustering studies, the proposed study resorts mainly to geometric modeling to achieve dimensionality reduction and focuses on normal adult corneas in an attempt to identify their natural groupings, possibly in combination with feature extraction methods. Geometric modeling was based on Zernike polynomials, known for their interpretative transparency and sufficiently accurate for normal corneas. Different types of clustering methods were evaluated in pretests to identify the most effective at producing neatly delimitated clusters that are clearly interpretable. Their evaluation was based on clustering scores (to identify the best number of clusters), polar charts and scatter plots (to visualize the modeling coefficients involved in each cluster), average elevation maps and average profile cuts (to visualize the average corneal surface of each cluster), and statistical cluster comparisons on different clinical parameters (to validate the findings in reference to the clinical literature). K-means, applied to geometrically modeled surfaces without feature extraction, produced the best clusters, both for natural and normalized surfaces. While the clusters produced with natural corneal surfaces were based on the corneal curvature, those produced with normalized surfaces were based on the corneal axis. In each case, the best number of clusters was four. The importance of curvature and axis as grouping criteria in corneal data distribution is discussed. The fourth study presented in this thesis (Chapter 5) explores the ML paradigm to verify whether accurate predictions of normal corneal shapes can be made from clinical data, and how. The database of normal adult corneal surfaces was first preprocessed by geometric modeling to reduce its dimensionality into short vectors of 12 to 20 Zernike coefficients, found to be in the range of appropriate numbers to achieve optimal predictions. The nonlinear regression methods examined from the scikit-learn library were gradient boosting, Gaussian process, kernel ridge, random forest, k-nearest neighbors, bagging, and multilayer perceptron. The predictors were based on the clinical variables available in the database, including geometric variables (best-fit sphere radius, white-towhite diameter, anterior chamber depth, corneal side), refraction variables (sphere, cylinder, axis) and demographic variables (age, gender). Each possible combination of regression method, set of clinical variables (used as predictors) and number of Zernike coefficients (used as targets) defined a regression model in a prediction test. All the regression models were evaluated based on their mean RMSE score (establishing the distance between the predicted corneal surfaces and the raw topographic true surfaces). The best model identified was further qualitatively assessed based on an atlas of predicted and true average elevation maps by which the predicted surfaces could be visually compared to the true surfaces on each of the clinical variables used as predictors. It was found that the best regression model was gradient boosting using all available clinical variables as predictors and 16 Zernike coefficients as targets. The most explicative predictor was the best-fit sphere radius, followed by the side and refractive variables. The average elevation maps of the true anterior corneal surfaces and the predicted surfaces based on this model were remarkably similar for each clinical variable.