[pt] Esquemas semi-Lagrangeanos usados para a aproximação do princípio da programação dinâmica são baseados em uma discretização temporal reconstruída no espaço de estado. O uso de uma malha estruturada torna essa abordagem inviável para problemas de alta dimensão devido à maldição da dimensionalidade. Nesta tese, apresentamos uma nova abordagem para problemas de controle ótimo de horizonte infinito onde a função valor é calculada usando Funções de Base Radial (RBFs) pelo método de aproximação de mínimos quadrados móveis de Shepard em malhas irregulares. Propomos um novo método para gerar uma malha irregular guiada pela dinâmica e uma rotina de otimizada para selecionar o parâmetro responsável pelo formato nas RBFs. Esta malha ajudará a localizar o problema e aproximar o princípio da programação dinâmica em alta dimensão. As estimativas de erro para a função valor também são fornecidas. Testes numéricos para problemas de alta dimensão mostrarão a eficácia do método proposto. Além do controle ótimo de EDPs clássicas mostramos como o método também pode ser aplicado ao controle de equações não-locais. Também fornecemos um exemplo analisando a convergência numérica de uma equação não-local controlada para o modelo contínuo. / [en] Semi-Lagrangian schemes for the approximation of the dynamic programming principle are based on a time discretization projected on a state-space grid. The use of a structured grid makes this approach not feasible for highdimensional problems due to the curse of dimensionality. In this thesis, we present a new approach for infinite horizon optimal control problems where the value function is computed using Radial Basis Functions (RBF) by the Shepard s moving least squares approximation method on scattered grids. We propose a new method to generate a scattered mesh driven by the dynamics and an optimal routine to select the shape parameter in the RBF. This mesh will help to localize the problem and approximate the dynamic programming principle in high dimension. Error estimates for the value function are also provided. Numerical tests for high dimensional problems will show the effectiveness of the proposed method. In addition to the optimal control of classical PDEs, we show how the method can also be applied to the control of nonlocal equations. We also provide an example analyzing the numerical convergence of a nonlocal controlled equation towards the continuous model.

[EN] This thesis concerns the analysis of differential equations with uncertain input parameters, in the form of random variables or stochastic processes with any type of probability distributions. In modeling, the input coefficients are set from experimental data, which often involve uncertainties from measurement errors. Moreover, the behavior of the physical phenomenon under study does not follow strict deterministic laws. It is thus more realistic to consider mathematical models with randomness in their formulation. The solution, considered in the sample-path or the mean square sense, is a smooth stochastic process, whose uncertainty has to be quantified. Uncertainty quantification is usually performed by computing the main statistics (expectation and variance) and, if possible, the probability density function. In this dissertation, we study random linear models, based on ordinary differential equations with and without delay and on partial differential equations. The linear structure of the models makes it possible to seek for certain probabilistic solutions and even approximate their probability density functions, which is a difficult goal in general. A very important part of the dissertation is devoted to random second-order linear differential equations, where the coefficients of the equation are stochastic processes and the initial conditions are random variables. The study of this class of differential equations in the random setting is mainly motivated because of their important role in Mathematical Physics. We start by solving the randomized Legendre differential equation in the mean square sense, which allows the approximation of the expectation and the variance of the stochastic solution. The methodology is extended to general random second-order linear differential equations with analytic (expressible as random power series) coefficients, by means of the so-called Fröbenius method. A comparative case study is performed with spectral methods based on polynomial chaos expansions. On the other hand, the Fröbenius method together with Monte Carlo simulation are used to approximate the probability density function of the solution. Several variance reduction methods based on quadrature rules and multilevel strategies are proposed to speed up the Monte Carlo procedure. The last part on random second-order linear differential equations is devoted to a random diffusion-reaction Poisson-type problem, where the probability density function is approximated using a finite difference numerical scheme. The thesis also studies random ordinary differential equations with discrete constant delay. We study the linear autonomous case, when the coefficient of the non-delay component and the parameter of the delay term are both random variables while the initial condition is a stochastic process. It is proved that the deterministic solution constructed with the method of steps that involves the delayed exponential function is a probabilistic solution in the Lebesgue sense. Finally, the last chapter is devoted to the linear advection partial differential equation, subject to stochastic velocity field and initial condition. We solve the equation in the mean square sense and provide new expressions for the probability density function of the solution, even in the non-Gaussian velocity case. / [ES] Esta tesis trata el análisis de ecuaciones diferenciales con parámetros de entrada aleatorios, en la forma de variables aleatorias o procesos estocásticos con cualquier tipo de distribución de probabilidad. En modelización, los coeficientes de entrada se fijan a partir de datos experimentales, los cuales suelen acarrear incertidumbre por los errores de medición. Además, el comportamiento del fenómeno físico bajo estudio no sigue patrones estrictamente deterministas. Es por tanto más realista trabajar con modelos matemáticos con aleatoriedad en su formulación. La solución, considerada en el sentido de caminos aleatorios o en el sentido de media cuadrática, es un proceso estocástico suave, cuya incertidumbre se tiene que cuantificar. La cuantificación de la incertidumbre es a menudo llevada a cabo calculando los principales estadísticos (esperanza y varianza) y, si es posible, la función de densidad de probabilidad. En este trabajo, estudiamos modelos aleatorios lineales, basados en ecuaciones diferenciales ordinarias con y sin retardo, y en ecuaciones en derivadas parciales. La estructura lineal de los modelos nos permite buscar ciertas soluciones probabilísticas e incluso aproximar su función de densidad de probabilidad, lo cual es un objetivo complicado en general. Una parte muy importante de la disertación se dedica a las ecuaciones diferenciales lineales de segundo orden aleatorias, donde los coeficientes de la ecuación son procesos estocásticos y las condiciones iniciales son variables aleatorias. El estudio de esta clase de ecuaciones diferenciales en el contexto aleatorio está motivado principalmente por su importante papel en la Física Matemática. Empezamos resolviendo la ecuación diferencial de Legendre aleatorizada en el sentido de media cuadrática, lo que permite la aproximación de la esperanza y la varianza de la solución estocástica. La metodología se extiende al caso general de ecuaciones diferenciales lineales de segundo orden aleatorias con coeficientes analíticos (expresables como series de potencias), mediante el conocido método de Fröbenius. Se lleva a cabo un estudio comparativo con métodos espectrales basados en expansiones de caos polinomial. Por otro lado, el método de Fröbenius junto con la simulación de Monte Carlo se utilizan para aproximar la función de densidad de probabilidad de la solución. Para acelerar el procedimiento de Monte Carlo, se proponen varios métodos de reducción de la varianza basados en reglas de cuadratura y estrategias multinivel. La última parte sobre ecuaciones diferenciales lineales de segundo orden aleatorias estudia un problema aleatorio de tipo Poisson de difusión-reacción, en el que la función de densidad de probabilidad es aproximada mediante un esquema numérico de diferencias finitas. En la tesis también se tratan ecuaciones diferenciales ordinarias aleatorias con retardo discreto y constante. Estudiamos el caso lineal y autónomo, cuando el coeficiente de la componente no retardada i el parámetro del término retardado son ambos variables aleatorias mientras que la condición inicial es un proceso estocástico. Se demuestra que la solución determinista construida con el método de los pasos y que involucra la función exponencial retardada es una solución probabilística en el sentido de Lebesgue. Finalmente, el último capítulo lo dedicamos a la ecuación en derivadas parciales lineal de advección, sujeta a velocidad y condición inicial estocásticas. Resolvemos la ecuación en el sentido de media cuadrática y damos nuevas expresiones para la función de densidad de probabilidad de la solución, incluso en el caso de velocidad no Gaussiana. / [CA] Aquesta tesi tracta l'anàlisi d'equacions diferencials amb paràmetres d'entrada aleatoris, en la forma de variables aleatòries o processos estocàstics amb qualsevol mena de distribució de probabilitat. En modelització, els coeficients d'entrada són fixats a partir de dades experimentals, les quals solen comportar incertesa pels errors de mesurament. A més a més, el comportament del fenomen físic sota estudi no segueix patrons estrictament deterministes. És per tant més realista treballar amb models matemàtics amb aleatorietat en la seua formulació. La solució, considerada en el sentit de camins aleatoris o en el sentit de mitjana quadràtica, és un procés estocàstic suau, la incertesa del qual s'ha de quantificar. La quantificació de la incertesa és sovint duta a terme calculant els principals estadístics (esperança i variància) i, si es pot, la funció de densitat de probabilitat. En aquest treball, estudiem models aleatoris lineals, basats en equacions diferencials ordinàries amb retard i sense, i en equacions en derivades parcials. L'estructura lineal dels models ens fa possible cercar certes solucions probabilístiques i inclús aproximar la seua funció de densitat de probabilitat, el qual és un objectiu complicat en general. Una part molt important de la dissertació es dedica a les equacions diferencials lineals de segon ordre aleatòries, on els coeficients de l'equació són processos estocàstics i les condicions inicials són variables aleatòries. L'estudi d'aquesta classe d'equacions diferencials en el context aleatori està motivat principalment pel seu important paper en Física Matemàtica. Comencem resolent l'equació diferencial de Legendre aleatoritzada en el sentit de mitjana quadràtica, el que permet l'aproximació de l'esperança i la variància de la solució estocàstica. La metodologia s'estén al cas general d'equacions diferencials lineals de segon ordre aleatòries amb coeficients analítics (expressables com a sèries de potències), per mitjà del conegut mètode de Fröbenius. Es duu a terme un estudi comparatiu amb mètodes espectrals basats en expansions de caos polinomial. Per altra banda, el mètode de Fröbenius juntament amb la simulació de Monte Carlo són emprats per a aproximar la funció de densitat de probabilitat de la solució. Per a accelerar el procediment de Monte Carlo, es proposen diversos mètodes de reducció de la variància basats en regles de quadratura i estratègies multinivell. L'última part sobre equacions diferencials lineals de segon ordre aleatòries estudia un problema aleatori de tipus Poisson de difusió-reacció, en què la funció de densitat de probabilitat és aproximada mitjançant un esquema numèric de diferències finites. En la tesi també es tracten equacions diferencials ordinàries aleatòries amb retard discret i constant. Estudiem el cas lineal i autònom, quan el coeficient del component no retardat i el paràmetre del terme retardat són ambdós variables aleatòries mentre que la condició inicial és un procés estocàstic. Es prova que la solució determinista construïda amb el mètode dels passos i que involucra la funció exponencial retardada és una solució probabilística en el sentit de Lebesgue. Finalment, el darrer capítol el dediquem a l'equació en derivades parcials lineal d'advecció, subjecta a velocitat i condició inicial estocàstiques. Resolem l'equació en el sentit de mitjana quadràtica i donem noves expressions per a la funció de densitat de probabilitat de la solució, inclús en el cas de velocitat no Gaussiana. / This work has been supported by the Spanish Ministerio de Economía y Competitividad grant MTM2017–89664–P. I acknowledge the doctorate scholarship granted by Programa de Ayudas de Investigación y Desarrollo (PAID), Universitat Politècnica de València. / Jornet Sanz, M. (2020). Mean square solutions of random linear models and computation of their probability density function [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/138394

The aim of this bachelor's thesis is to use three variants of the discrete Fourier transform (DFT) and compare their computational cost. The transformation will be used to numerically solve partial differential equations (PDE). In its simplest form, the DFT can be regarded as a matrix multiplication. It turns out that this matrix has some nice properties that we can exploit. Namely that it is well-conditioned and the inverse of the matrix elements is similar to the original matrix element, which will simplifies the implementation. Also, the matrix can be rewritten using different properties of complex numbers to reduce computational cost. It turns out that each transformation method has its own benefits and drawbacks. One of the methods makes the cost lower but can only use data of a fixed size. Another method needs a specific library to work but is way faster than the other two methods. The type of PDE that will be solved in this thesis are advection and diffusion, which aided by the Fourier transform, can be rewritten as a set of ordinary differential equations (ODE). These ODEs can then be integrated in time with a Runge-Kutta method. / Detta kandidatarbete går ut på att betrakta tre olika diskreta fouriertransformer och jämföra deras beräkningstid. Fouriertransformen används sedan också för att lösa partiella differentialekvationer (PDE). Fouriertransformerna som betraktas kan ses som en matrismultiplikation. Denna matrismultiplikation visar sig har trevliga egenskaper. Nämligen att matrisen är välkonditionerad och att matrisinversen element liknar ursprungsmatrisens element, vilket kommer underlätta implementationen. Matrisen kan dessutom skrivas om genom diverse samband hos komplexa tal för att få snabbare beräkningstid. PDE:na som betraktas i detta kandiatarbete är advektions och diffusions, vilket med speciella antaganden kan skrivas om till en ordinär differentialekvation som löses med en Runge-Kutta metod. Fouriertransformen används för att derivera, då det motsvarar en multiplikation. Det visar sig att alla metoder har fördelar och nackdelar. Ena metoden gör beräkningen snabbare men kan endast använda sig av datamängder av viss storlek. Andra metoden kräver ett specifikt bibliotek för att fungera men är mycket snabbare än de andra två.

Un algorithme permettant de discrétiser les équations aux dérivées partielles (EDP) tout en préservant leurs symétries de Lie est élaboré. Ceci est rendu possible grâce à l'utilisation de dérivées partielles discrètes se transformant comme les dérivées partielles continues sous l'action de groupes de Lie locaux. Dans les applications, beaucoup d'EDP sont invariantes sous l'action de transformations ponctuelles de Lie de dimension infinie qui font partie de ce que l'on désigne comme des pseudo-groupes de Lie. Afin d'étendre la méthode de discrétisation préservant les symétries à ces équations, une discrétisation des pseudo-groupes est proposée. Cette discrétisation a pour effet de transformer les symétries ponctuelles en symétries généralisées dans l'espace discret. Des schémas invariants sont ensuite créés pour un certain nombre d'EDP. Dans tous les cas, des tests numériques montrent que les schémas invariants approximent mieux leur équivalent continu que les différences finies standard. / An algorithm discretizing partial differential equations (PDEs) while preserving their Lie symmetries is provided. This is made possible by the use of discrete partial derivatives transforming as their continuous counterparts under the action of local Lie groups. In applications, many PDEs are invariant under the action of Lie point symmetries of infinite dimension designated as Lie pseudo-groups. To extend the invariant discretization method to such equations, a discretization of pseudo-groups is proposed. The pseudo-group action discretization transforms the continuous point symmetries into generalized symmetries in the discrete space. Invariant schemes are then created for a number of PDEs. In all cases, numerical tests demonstrate that invariant schemes are better approximations of their continuous equivalents than standard finite differences.

Eine Vielzahl von Anwendungen in der numerischen Simulation der Strömungsdynamik und der Festkörpermechanik begründen die Entwicklung von zuverlässigen und effizienten Algorithmen für nicht-standard Methoden der Finite-Elemente-Methode (FEM). Um Freiheitsgrade zu sparen, wird in jedem Durchlauf des adaptiven Algorithmus lediglich ein Teil der Gebiete verfeinert. Einige Gebiete bleiben daher möglicherweise verhältnismäßig grob. Die Analyse der Konvergenz und vor allem die der Optimalität benötigt daher über die a priori Fehleranalyse hinausgehende Argumente. Etablierte adaptive Algorithmen beruhen auf collective marking, d.h. die zu verfeinernden Gebiete werden auf Basis eines Gesamtfehlerschätzers markiert. Bei adaptiven Algorithmen mit separate marking wird der Gesamtfehlerschätzer in einen Volumenterm und in einen Fehlerschätzerterm aufgespalten. Da der Volumenterm unabhängig von der diskreten Lösung ist, kann einer schlechten Datenapproximation durch eine lokal tiefe Verfeinerung begegnet werden. Bei hinreichender Datenapproximation wird das Gitter dagegen bezüglich des neuen Fehlerschätzerterms wie üblich level-orientiert verfeinert. Die numerischen Experimente dieser Arbeit liefern deutliche Indizien der quasi-optimalen Konvergenz für den in dieser Arbeit untersuchten adaptiven Algorithmus, der auf separate marking beruht. Der Parameter, der die Verbesserung der Datenapproximation sicherstellt, ist frei wählbar. Dadurch ist es erstmals möglich, eine ausreichende und gleichzeitig optimale Approximation der Daten innerhalb weniger Durchläufe zu erzwingen. Diese Arbeit ermöglicht es, Standardargumente auch für die Konvergenzanalyse von Algorithmen mit separate marking zu verwenden. Dadurch gelingt es Quasi-Optimalität des vorgestellten Algorithmus gemäß einer generellen Vorgehensweise für die drei Beispiele, dem Poisson Modellproblem, dem reinen Verschiebungsproblem der linearen Elastizität und dem Stokes Problem, zu zeigen. / Various applications in computational fluid dynamics and solid mechanics motivate the development of reliable and efficient adaptive algorithms for nonstandard finite element methods (FEMs). To reduce the number of degrees of freedom, in adaptive algorithms only a selection of finite element domains is marked for refinement on each level. Since some element domains may stay relatively coarse, even the analysis of convergence and more importantly the analysis of optimality require new arguments beyond an a priori error analysis. In adaptive algorithms, based on collective marking, a (total) error estimator is used as refinement indicator. For separate marking strategies, the (total) error estimator is split into a volume term and an error estimator term, which estimates the error. Since the volume term is independent of the discrete solution, if there is a poor data approximation the improvement may be realised by a possibly high degree of local mesh refinement. Otherwise, a standard level-oriented mesh refinement based on an error estimator term is performed. This observation results in a natural adaptive algorithm based on separate marking, which is analysed in this thesis. The results of the numerical experiments displayed in this thesis provide strong evidence for the quasi-optimality of the presented adaptive algorithm based on separate marking and for all three model problems. Furthermore its flexibility (in particular the free steering parameter for data approximation) allows a sufficient and optimal data approximation in just a few number of levels of the adaptive scheme. This thesis adapts standard arguments for optimal convergence to adaptive algorithms based on separate marking with a possibly high degree of local mesh refinement, and proves quasi-optimality following a general methodology for three model problems, i.e., the Poisson model problem, the pure displacement problem in linear elasticity and the Stokes equations.

Diese Arbeit behandelt garantierte Fehlerkontrolle für elliptische partielle Differentialgleichungen anhand des Poisson-Modellproblems, des Stokes-Problems und des Hindernisproblems. Hierzu werden garantierte obere Schranken für den Energiefehler zwischen exakter Lösung und diskreten Finite-Elemente-Approximationen erster Ordnung entwickelt. Ein verallgemeinerter Ansatz drückt den Energiefehler durch Dualnormen eines oder mehrerer Residuen aus. Hinzu kommen berechenbare Zusatzterme, wie Oszillationen der gegebenen Daten, mit expliziten Konstanten. Für die Abschätzung der Dualnormen der Residuen existieren viele verschiedene Techniken. Diese Arbeit beschäftigt sich vorrangig mit Equilibrierungsschätzern, basierend auf Raviart-Thomas-Elementen, welche effiziente garantierte obere Schranken ermöglichen. Diese Schätzer werden mit einem Postprocessing-Verfahren kombiniert, das deren Effizienz mit geringem zusätzlichen Rechenaufwand deutlich verbessert. Nichtkonforme Finite-Elemente-Methoden erzeugen zusätzlich ein Inkonsistenzresiduum, dessen Dualnorm mit Hilfe diverser konformer Approximationen abgeschätzt wird. Ein Nebenaspekt der Arbeit betrifft den expliziten residuen-basierten Fehlerschätzer, der für gewöhnlich optimale und leicht zu berechnende Verfeinerungsindikatoren für das adaptive Netzdesign liefert, aber nur schlechte garantierte obere Schranken. Eine neue Variante, die auf den equilibrierten Flüssen des Luce-Wohlmuth-Fehlerschätzers basiert, führt zu stark verbesserten Zuverlässigkeitskonstanten. Eine Vielzahl numerischer Experimente vergleicht alle implementierten Fehlerschätzer und zeigt, dass effiziente und garantierte Fehlerkontrolle in allen vorliegenden Modellproblemen möglich ist. Insbesondere zeigt ein Modellproblem, wie die Fehlerschätzer erweitert werden können, um auch auf Gebieten mit gekrümmten Rändern garantierte obere Schranken zu liefern. / This thesis studies guaranteed error control for elliptic partial differential equations on the basis of the Poisson model problem, the Stokes equations and the obstacle problem. The error control derives guaranteed upper bounds for the energy error between the exact solution and different finite element discretisations, namely conforming and nonconforming first-order approximations. The unified approach expresses the energy error by dual norms of one or more residuals plus computable extra terms, such as oscillations of the given data, with explicit constants. There exist various techniques for the estimation of the dual norms of such residuals. This thesis focuses on equilibration error estimators based on Raviart-Thomas finite elements, which permit efficient guaranteed upper bounds. The proposed postprocessing in this thesis considerably increases their efficiency at almost no additional computational costs. Nonconforming finite element methods also give rise to a nonconsistency residual that permits alternative treatment by conforming interpolations. A side aspect concerns the explicit residual-based error estimator that usually yields cheap and optimal refinement indicators for adaptive mesh refinement but not very sharp guaranteed upper bounds. A novel variant of the residual-based error estimator, based on the Luce-Wohlmuth equilibration design, leads to highly improved reliability constants. A large number of numerical experiments compares all implemented error estimators and provides evidence that efficient and guaranteed error control in the energy norm is indeed possible in all model problems under consideration. Particularly, one model problem demonstrates how to extend the error estimators for guaranteed error control on domains with curved boundary.

This thesis is concerned with the investigation of numerical methods for the solution of the Hamilton-Jacobi-Bellman (HJB) equations arising in European and American option pricing with proportional transaction costs. We first consider the problem of computing reservation purchase and write prices of a European option in the model proposed by Davis, Panas and Zariphopoulou [19]. It has been shown [19] that computing the reservation purchase and write prices of a European option involves solving three different fully nonlinear HJB equations. In this thesis, we propose a penalty approach combined with a finite difference scheme to solve the HJB equations. We first approximate each of the HJB equations by a quasi-linear second order partial differential equation containing two linear penalty terms with penalty parameters. We then develop a numerical scheme based on the finite differencing in both space and time for solving the penalized equation. We prove that there exists a unique viscosity solution to the penalized equation and the viscosity solution to the penalized equation converges to that of the original HJB equation as the penalty parameters tend to infinity. We also prove that the solution of the finite difference scheme converges to the viscosity solution of the penalized equation. Numerical results are given to demonstrate the effectiveness of the proposed method. We extend the penalty approach combined with a finite difference scheme to the HJB equations in the American option pricing model proposed by Davis and Zarphopoulou [20]. Numerical experiments are presented to illustrate the theoretical findings.

Made available in DSpace on 2014-06-11T19:26:56Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-02-25Bitstream added on 2014-06-13T19:06:36Z : No. of bitstreams: 1 casaca_wco_me_sjrp.pdf: 5215634 bytes, checksum: 291e2a21fdb4d46a11de22f18cc97f93 (MD5) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / Neste trabalho propomos quatro novas abordagens para tratar o problema de restauração de imagens reais contendo texturas sob a perspectiva dos temas: reconstrução de regiões danificadas, remoção de objetos, e eliminação de ruídos. As duas primeiras abor dagens são designadas para recompor partes perdias ou remover objetos de uma imagem real a partir de formulações envolvendo decomposiçãode imagens e inpainting por exem- plar, enquanto que as duas últimas são empregadas para remover ruído, cujas formulações são baseadas em decomposição de três termos e equações diferenciais parciais não lineares. Resultados experimentais atestam a boa performace dos protótipos apresentados quando comparados à modelagens correlatas da literatura. / In this paper we propose four new approaches to address the problem of restoration of real images containing textures from the perspective of reconstruction of damaged areas, object removal, and denoising topics. The first two approaches are designed to reconstruct missing parts or to remove objects of a real image using formulations based on image de composition and exemplar based inpainting, while the last two other approaches are used to remove noise, whose formulations are based on decomposition of three terms and non- linear partial di®erential equations. Experimental results attest to the good performance of the presented prototypes when compared to modeling related in literature.

An adaptive mesh redistribution method for efficient and accurate simulation of multi dimensional hyperbolic conservation laws is developed. The algorithm consists of two coupled steps; evolution of the governing PDE followed by a redistribution of the computational nodes. The second step, i.e. mesh redistribution is carried out at each time step iteratively with the primary aim of adapting the grid to the computed solution in order to maximize accuracy while minimizing the computational overheads. The governing hyperbolic conservation laws, originally defined on the physical domain, are transformed on to a simplified computational domain where the position of the nodes remains independent of time. The transformed governing hyperbolic equations are recast in a strong conservative form and are solved directly on the computational domain without the need for interpolation that is typically associated with standard mesh redistribution algorithms. Several standard test cases involving numerical solution of scalar and system of hyperbolic conservation laws in one and two dimensions are presented in order to demonstrate the accuracy and computational efficiency of the proposed technique.

Dans les domaines de l’imagerie médicale et de la vision par ordinateur, la segmentation joue un rôle crucial dans le but d’extraire les composantes intéressantes d’une image ou d’une séquence d’images. Elle est à l’intermédiaire entre le traitement d’images de bas niveau et les applications cliniques et celles de la vision par ordinateur de haut niveau. Ces applications de haut niveau peuvent inclure le diagnostic, la planification de la thérapie, la détection et la reconnaissance d'objet, etc. Parmi les méthodes de segmentation existantes, les courbes géodésiques minimales possèdent des avantages théoriques et pratiques importants tels que le minimum global de l’énergie géodésique et la méthode bien connue de Fast Marching pour obtenir une solution numérique. Dans cette thèse, nous nous concentrons sur les méthodes géodésiques basées sur l’équation aux dérivées partielles, l’équation Eikonale, afin d’étudier des méthodes précises, rapides et robustes, pour l’extraction de structures tubulaires et la segmentation d’image, en développant diverses métriques géodésiques locales pour des applications cliniques et la segmentation d’images en général. / In the fields of medical imaging and computer vision, segmentation plays a crucial role with the goal of separating the interesting components from one image or a sequence of image frames. It bridges the gaps between the low-level image processing and high level clinical and computer vision applications. Among the existing segmentation methods, minimal geodesics have important theoretical and practical advantages such as the global minimum of the geodesic energy and the well-established fast marching method for numerical solution. In this thesis, we focus on the Eikonal partial differential equation based geodesic methods to investigate accurate, fast and robust tubular structure extraction and image segmentation methods, by developing various local geodesic metrics for types of clinical and segmentation tasks. This thesis aims to applying different geodesic metrics based on the Eikonal framework to solve different image segmentation problems especially for tubularity segmentation and region-based active contours models, by making use of more information from the image feature and prior clinical knowledges. The designed geodesic metrics basically take advantages of the geodesic orientation or anisotropy, the image feature consistency, the geodesic curvature and the geodesic asymmetry property to deal with various difficulties suffered by the classical minimal geodesic models and the active contours models. The main contributions of this thesis lie at the deep study of the various geodesic metrics and their applications in medical imaging and image segmentation. Experiments on medical images and nature images show the effectiveness of the presented contributions.