Les travaux réalisés dans cette thèse concernent l’étude de problèmes quasi-linéaires paraboliques et elliptiques singuliers. Par singularité, nous signifions que le problème fait intervenir une non linéarité qui explose au bord du domaine où l’équation est posée. La présence du terme singulier entraine un manque de régularité des solutions. Ce défaut de régularité génère en conséquence un manque de compacité qui ne permet pas d’appliquer directement les méthodes classiques d’analyse non linéaires pour démontrer l’existence de solutions et discuter les propriétés de régularité et de comportement asymptotique des solutions. Pour contourner cette difficulté dans le contexte des problèmes que nous avons étudiés, nous sommes amenés à établir des estimations a priori très fines au voisinage du bord en combinant diverses méthodes : méthodes de monotonie (reliées au principe du maximum), méthodes variationnelles, argument de convexité, méthodes d’interpolation dans les espaces de Sobolev, méthodes de point fixe. / In this thesis I have studied the Evolution p-laplacian equation with singular nonlinearity. We start by studying the corresponding elliptic problem and then by defining a proper cone in a suitable Sobolev space find the uniqueness of the solution. Taking that into account and using the semi discretization in time we arrive at the uniqueness and existence result. Next we prove some regularity theorem using tools from Nonlinear Semigroup theory and Interpolation spaces. We also establish some related result for the laplacian case where we improve our result on the existence and regularity, due to the non degeneracy of the laplacian. In another related work we work with a semilinear equation with singular nonlinearity and using the moving plane method prove the symmetry properties of any classical solution. We also give some related apriori estimates which together with the symmetry provide us the existence of solution using the bifurcation result.

The work in this thesis is inspired by the fabrication of Zn₄₅Au₃₀Cu₂₅. This is the first alloy undergoing ultra-reversible martensitic transformations and closely satisfying the cofactor conditions, particular conditions of geometric compatibility between phases, which were conjectured to influence reversibility. With the aim of better understanding reversibility, in this thesis we study the martensitic microstructures arising during thermal cycling in Zn₄₅Au₃₀Cu₂₅, which are complex and different in every phase transformation cycle. Our study is developed in the context of continuum mechanics and nonlinear elasticity, and we use tools from nonlinear analysis. The first aim of this thesis is to advance our understanding of conditions of geometric compatibility between phases. To this end, first, we further investigate cofactor conditions and introduce a physically-based metric to measure how closely these are satisfied in real materials. Secondly, we introduce further conditions of compatibility and show that these are nearly satisfied by some twins in Zn₄₅Au₃₀Cu₂₅. These might influence reversibility as they improve compatibility between high and low temperature phases. Martensitic phase transitions in Zn₄₅Au₃₀Cu₂₅ are a complex phenomenon, especially because the crystalline structure of the material changes from a cubic to a monoclinic symmetry, and hence the energy of the system has twelve wells. There exist infinitely many energy-minimising microstructures, limiting our understanding of the phenomenon as well as our ability to predict it. Therefore, the second aim of this thesis is to find criteria to select physically-relevant energy minimisers. We introduce two criteria or selection mechanisms. The first involves a moving mask approximation, which allows one to describe some experimental observations on the dynamics, while the second is based on using vanishing interface energy. The moving mask approximation reflects the idea of a moving curtain covering and uncovering microstructures during the phase transition, as appears to be the case for Zn₄₅Au₃₀Cu₂₅, and many other materials during thermally induced transformations. We show that the moving mask approximation can be framed in the context of a model for the dynamics of nonlinear elastic bodies. We prove that every macroscopic deformation gradient satisfying the moving mask approximation must be of the form 1 + a(x) ⊗ n(x), for a.e. x. With regards to vanishing interface energy, we consider a one-dimensional energy functional with three wells, which simplifies the physically relevant model for martensitic transformations, but at the same time highlights some key issues. Our energy functional admits infinitely many minimising gradient Young measures, representing energy-minimising microstructures. In order to select the physically relevant ones, we show that minimisers of a regularised energy, where the second derivatives are penalised, generate a unique minimising gradient Young measure as the perturbation vanishes. The results developed in this thesis are motivated by the study of Zn₄₅Au₃₀Cu₂₅, but their relevance is not limited to this material. The results on the cofactor conditions developed here can help for the understanding of new alloys undergoing ultra-reversible transformations, and as a guideline for the fabrication of future materials. Furthermore, the selection mechanisms studied in this work can be useful in selecting physically relevant microstructures not only in Zn₄₅Au₃₀Cu₂₅, but also in other materials undergoing martensitic transformations, and other phenomena where pattern formation is observed.

We approximate the solution of a quasilinear stochastic partial differential equa- tion driven by fractional Brownian motion B_H(t); H in (0,1), which was calculated via fractional White Noise calculus, see [5].

[pt] Esse trabalho tem como objetivo ser uma introdução ao estudo da existência e unicidade de soluções fracas para equações diferenciais parciais elípticas. Começamos definindo o espaço de Sobolev para, a partir da definição, provarmos algumas propriedades básicas que nos ajudarão no estudo das equações diferenciais parciais elípticas. Finalizamos com o desenvolvimento do Teorema de Lax-Milgram e de Stampacchia que permitirão o uso de técnicas de Análise Funcional para estudarmos alguns exemplos de equações elípticas. / [en] This dissertation aims to be an introduction to the study of the existence and uniqueness of weak solutions for elliptic partial differential equations. We begin by defining the Sobolev spaces and proving some basics properties that will assist in the study of the elliptical equations. Lastly, we develop the Theorems of Lax-Milgram and Stampacchia that allow the use of Functional Analysis for the studying of some examples of elliptic equations.

abstract: In order to verify the dispersive nature of transverse displacement in a beam, a deep understanding of the governing partial differential equation is developed. Using the finite element method and Newmark’s method, along with Fourier transforms and other methods, the aim is to obtain consistent results across each numerical technique. An analytical solution is also analyzed for the Euler-Bernoulli beam in order to gain confidence in the numerical techniques when used for more advance beam theories that do not have a known analytical solution. Three different beam theories are analyzed in this report: The Euler-Bernoulli beam theory, Rayleigh beam theory and Timoshenko beam theory. A comparison of the results show the difference between each theory and the advantages of using a more advanced beam theory for higher frequency vibrations. / Dissertation/Thesis / Masters Thesis Civil Engineering 2016

Submitted by Izabel Franco (izabel-franco@ufscar.br) on 2016-09-23T14:42:46Z No. of bitstreams: 1 DissPLS.pdf: 887262 bytes, checksum: e54f2438d019bad9fa31a2f0e8b98d66 (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-09-26T20:40:36Z (GMT) No. of bitstreams: 1 DissPLS.pdf: 887262 bytes, checksum: e54f2438d019bad9fa31a2f0e8b98d66 (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-09-26T20:40:46Z (GMT) No. of bitstreams: 1 DissPLS.pdf: 887262 bytes, checksum: e54f2438d019bad9fa31a2f0e8b98d66 (MD5) / Made available in DSpace on 2016-09-26T20:40:52Z (GMT). No. of bitstreams: 1 DissPLS.pdf: 887262 bytes, checksum: e54f2438d019bad9fa31a2f0e8b98d66 (MD5) Previous issue date: 2015-04-24 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / The Rosenau-Hyman, or K(m, n), equations are a generalized version of the Korteweg-de Vries (KdV) equation where the dipersive term is non-linear. Such partial differential equations not always have a specific method by which can be solved, besides the solutions are not always analytical. The Lie symmetry method was applied to look for solutions of these equations. This method consists in finding the most general symmetry group of the equation, wherewith the solution can be found. It was found an expression to the solution and to some particular cases. It was shown that in the case K(2, 2) a new kind of solution, called compacton, with peculiar properties is found. / Equações Rosenau-Hyman, ou K(m, n), são uma versão generalizada da equação Kortewegde Vries (KdV) em que o termo dispersivo é não-linear. Essas equações diferencias nãolineares nem sempre possuem um método específico pelo qual podem ser resolvidas, além de que as soluções nem sempre são analíticas. O método de simetria de Lie foi aplicado para buscar por soluções dessas equações. Esse método consiste em encontrar o grupo de simetria mais geral da equação, por meio do qual a solução pode ser encontrada. Obteve-se uma expressão para a solução e alguns casos particulares. Foi mostrado que para K(2, 2) um novo tipo de solução, chamada compacton, com propriedades peculiares é encontrado.

Submitted by Maike Costa (maiksebas@gmail.com) on 2016-03-29T13:33:49Z No. of bitstreams: 1 arquivo total.pdf: 5320246 bytes, checksum: 158dd460a20ce46c96d4a34623612264 (MD5) / Made available in DSpace on 2016-03-29T13:33:49Z (GMT). No. of bitstreams: 1 arquivo total.pdf: 5320246 bytes, checksum: 158dd460a20ce46c96d4a34623612264 (MD5) Previous issue date: 2011-08-25 / This work is mainly concerned with the existence and nonexistence of classical solution to the nonhomogeneous semilinear equation Δu + up + f(x) = 0 in Rn, u > 0 in Rn, when n 3, where f 0 is a Hölder continuous function. The nonexistence of classical solution is established when 1 < p n=(n 􀀀 2). For p > n=(n 􀀀 2) there may be both existence and nonexistence results depending on the asymptotic behavior of f at infinity. The existence results were obtained by employed sub and supersolutions techniques and fixed point theorem. For the nonexistence of classical solution we used a priori integral estimates obtained via averaging. / Neste trabalho, estamos interessados na existência e não existência de solução clássica para a equação não homogênea semilinear Δu + up + f(x) = 0 em Rn; u > 0 em Rn, n 3 onde f 0 é uma função Hölder contínua. A não existência de solução clássica é estabelecida quando 1 < p n=(n 􀀀 2). Para p > n=(n 􀀀 2), temos resultados de existência e não existência de solução clássica, dependendo do comportamento assin- tótico de f no infinito. Os resultados de existência foram obtidos usando o método de sub e supersolução e teoremas de ponto fixo. A não existência de solução clássica é obtida usando-se estimativas integrais a priori via média esférica.

Orientador: Heloisa Helena Marino Silva / Banca: Antonio Castelo Filho / Banca: Maurílio Boaventura / Resumo: O objetivo deste trabalho þe apresentar um estudo sobre a aplicação de métodos numéricos para a resolução do modelo proposto por Barcelos, Boaventura e Silva Jr. [7], para a eliminação de ruídos em imagens digitais por meio de uma equação diferencial parcial, e propor uma anþalise da estabilidade do mþetodo iterativo comumente aplicado a este modelo. Uma anþalise comparativa entre os vários mþetodos abordados þe realizada atravþes de resultados experimentais em imagens sintéticas e imagens da vida real. / Abstract: The purpose of this work is to present a study on the application of numerical methods for the resolution of model considered by Barcelos, Boaventura and Silva Jr [7], for image denoising through a partial di erential equation, and to consider a stability analysis of an iterative method usually applied to this model. A comparative analysis among various considered methods is carried out through experimental results for synthetic and real images. / Mestre

Orientadores: Jose Mario Martinez, Sandra Augusta Santos / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica / Made available in DSpace on 2018-08-12T03:24:09Z (GMT). No. of bitstreams: 1 Pisnitchenko_Fedor_D.pdf: 2087061 bytes, checksum: a87c208fa0681c4ecec62408f70f29ae (MD5) Previous issue date: 2008 / Resumo: Em ciência e engenharia existe uma vasta classe de problemas que consistem em resolver um sistema de equações diferenciais parciais para encontrar as variáveis (como velocidade, temperatura, deslocamento, etc), dada a informação de decisão necessária (como domínio, condições iniciais e de contorno, etc). Entretanto, para os problemas reais são muito comuns situações em que a informação de decisão seja incompleta e contenha erros, e, por outro lado, exista alguma informação sobre as variáveis de estado, obtida de uma outra simulação ou de algum tipo de observação (dados observados). Uma forma natural de resolver esse tipo de problema, utilizando toda a informação de decisão, é interpretá-lo como um problema de otimização. Ou seja, minimizar alguma função objetivo escolhida como a distância entre os dados observados e as variáveis de estado, sujeito à discretização do sistema. Neste trabalho propomos um método Quase-Newton para resolver o problema EDP restrito utilizando como modelos a equação unidimensional de Rossby-Obukhov e a equação de Kortewegde Vries. Um aspecto muito importante do método é não ter restrição de estabilidade para escolha dos passos na discretização das equações diferenciais. Um outro é poder utilizar passos maiores, em comparação com os métodos tradicionais evolutivos como diferenças finitas. Foi realizado um grande número de testes computacionais. Os resultados obtidos foram muito promissores, mostrando a robustez do método e a possibilidade de resolver problemas de grande porte. / Abstract: In science and engeneering there is a wide class of problems that consist in solving a system of partial differential equations to find variables (such as velocity, temperature, displacement, etc.), given the necessary decision information (such as domain, initial and boundary conditions, etc.). However,it is very common for real problems that the decision information is incomplete and contains errors. On the other hand, there is some additional information about state variables, which come from other simulation or some kind of observations (observed data). A natural way to solve this kind of problem, using all the decision information, is to interpret it as an optimization problem. That is, minimize an objective function chosen such as distance between the observed data and the state variables, subject to the system discretization. In this work, we propose a Quasi-Newton method to solve the PDE-constrained problem using as models the unidimensional Rossby-Obukhov and Korteweg-de Vries equations. A very importante aspect of the method is that there is no stability restriction for the stepsize in the differential equations discretization. Another aspect is to be able to use stepsizes larger than the ones used in traditional evolutive methods such as finite differences. A large number of computational test was performed. The results were promising and showed the robustness of the method and its ability to solve large scale problems. / Doutorado / Otimização / Doutor em Matemática Aplicada

[EN] Mathematical models based on deterministic differential equations do not take into account the inherent uncertainty of the physical phenomenon (in a wide sense) under study. In addition, inaccuracies in the collected data often arise due to errors in the measurements. It thus becomes necessary to treat the input parameters of the model as random quantities, in the form of random variables or stochastic processes. This gives rise to the study of random ordinary and partial differential equations. The computation of the probability density function of the stochastic solution is important for uncertainty quantification of the model output. Although such computation is a difficult objective in general, certain stochastic expansions for the model coefficients allow faithful representations for the stochastic solution, which permits approximating its density function. In this regard, Karhunen-Loève and generalized polynomial chaos expansions become powerful tools for the density approximation. Also, methods based on discretizations from finite difference numerical schemes permit approximating the stochastic solution, therefore its probability density function. The main part of this dissertation aims at approximating the probability density function of important mathematical models with uncertainties in their formulation. Specifically, in this thesis we study, in the stochastic sense, the following models that arise in different scientific areas: in Physics, the model for the damped pendulum; in Biology and Epidemiology, the models for logistic growth and Bertalanffy, as well as epidemiological models; and in Thermodynamics, the heat partial differential equation. We rely on Karhunen-Loève and generalized polynomial chaos expansions and on finite difference schemes for the density approximation of the solution. These techniques are only applicable when we have a forward model in which the input parameters have certain probability distributions already set. When the model coefficients are estimated from collected data, we have an inverse problem. The Bayesian inference approach allows estimating the probability distribution of the model parameters from their prior probability distribution and the likelihood of the data. Uncertainty quantification for the model output is then carried out using the posterior predictive distribution. In this regard, the last part of the thesis shows the estimation of the distributions of the model parameters from experimental data on bacteria growth. To do so, a hybrid method that combines Bayesian parameter estimation and generalized polynomial chaos expansions is used. / [ES] Los modelos matemáticos basados en ecuaciones diferenciales deterministas no tienen en cuenta la incertidumbre inherente del fenómeno físico (en un sentido amplio) bajo estudio. Además, a menudo se producen inexactitudes en los datos recopilados debido a errores en las mediciones. Por lo tanto, es necesario tratar los parámetros de entrada del modelo como cantidades aleatorias, en forma de variables aleatorias o procesos estocásticos. Esto da lugar al estudio de las ecuaciones diferenciales aleatorias. El cálculo de la función de densidad de probabilidad de la solución estocástica es importante en la cuantificación de la incertidumbre de la respuesta del modelo. Aunque dicho cálculo es un objetivo difícil en general, ciertas expansiones estocásticas para los coeficientes del modelo dan lugar a representaciones fieles de la solución estocástica, lo que permite aproximar su función de densidad. En este sentido, las expansiones de Karhunen-Loève y de caos polinomial generalizado constituyen herramientas para dicha aproximación de la densidad. Además, los métodos basados en discretizaciones de esquemas numéricos de diferencias finitas permiten aproximar la solución estocástica, por lo tanto, su función de densidad de probabilidad. La parte principal de esta disertación tiene como objetivo aproximar la función de densidad de probabilidad de modelos matemáticos importantes con incertidumbre en su formulación. Concretamente, en esta memoria se estudian, en un sentido estocástico, los siguientes modelos que aparecen en diferentes áreas científicas: en Física, el modelo del péndulo amortiguado; en Biología y Epidemiología, los modelos de crecimiento logístico y de Bertalanffy, así como modelos de tipo epidemiológico; y en Termodinámica, la ecuación en derivadas parciales del calor. Utilizamos expansiones de Karhunen-Loève y de caos polinomial generalizado y esquemas de diferencias finitas para la aproximación de la densidad de la solución. Estas técnicas solo son aplicables cuando tenemos un modelo directo en el que los parámetros de entrada ya tienen determinadas distribuciones de probabilidad establecidas. Cuando los coeficientes del modelo se estiman a partir de los datos recopilados, tenemos un problema inverso. El enfoque de inferencia Bayesiana permite estimar la distribución de probabilidad de los parámetros del modelo a partir de su distribución de probabilidad previa y la verosimilitud de los datos. La cuantificación de la incertidumbre para la respuesta del modelo se lleva a cabo utilizando la distribución predictiva a posteriori. En este sentido, la última parte de la tesis muestra la estimación de las distribuciones de los parámetros del modelo a partir de datos experimentales sobre el crecimiento de bacterias. Para hacerlo, se utiliza un método híbrido que combina la estimación de parámetros Bayesianos y los desarrollos de caos polinomial generalizado. / [CA] Els models matemàtics basats en equacions diferencials deterministes no tenen en compte la incertesa inherent al fenomen físic (en un sentit ampli) sota estudi. A més a més, sovint es produeixen inexactituds en les dades recollides a causa d'errors de mesurament. Es fa així necessari tractar els paràmetres d'entrada del model com a quantitats aleatòries, en forma de variables aleatòries o processos estocàstics. Açò dóna lloc a l'estudi de les equacions diferencials aleatòries. El càlcul de la funció de densitat de probabilitat de la solució estocàstica és important per a quantificar la incertesa de la sortida del model. Tot i que, en general, aquest càlcul és un objectiu difícil d'assolir, certes expansions estocàstiques dels coeficients del model donen lloc a representacions fidels de la solució estocàstica, el que permet aproximar la seua funció de densitat. En aquest sentit, les expansions de Karhunen-Loève i de caos polinomial generalitzat esdevenen eines per a l'esmentada aproximació de la densitat. A més a més, els mètodes basats en discretitzacions mitjançant esquemes numèrics de diferències finites permeten aproximar la solució estocàstica, per tant la seua funció de densitat de probabilitat. La part principal d'aquesta dissertació té com a objectiu aproximar la funció de densitat de probabilitat d'importants models matemàtics amb incerteses en la seua formulació. Concretament, en aquesta memòria s'estudien, en un sentit estocàstic, els següents models que apareixen en diferents àrees científiques: en Física, el model del pèndol amortit; en Biologia i Epidemiologia, els models de creixement logístic i de Bertalanffy, així com models de tipus epidemiològic; i en Termodinàmica, l'equació en derivades parcials de la calor. Per a l'aproximació de la densitat de la solució, ens basem en expansions de Karhunen-Loève i de caos polinomial generalitzat i en esquemes de diferències finites. Aquestes tècniques només són aplicables quan tenim un model cap avant en què els paràmetres d'entrada tenen ja determinades distribucions de probabilitat. Quan els coeficients del model s'estimen a partir de les dades recollides, tenim un problema invers. L'enfocament de la inferència Bayesiana permet estimar la distribució de probabilitat dels paràmetres del model a partir de la seua distribució de probabilitat prèvia i la versemblança de les dades. La quantificació de la incertesa per a la resposta del model es fa mitjançant la distribució predictiva a posteriori. En aquest sentit, l'última part de la tesi mostra l'estimació de les distribucions dels paràmetres del model a partir de dades experimentals sobre el creixement de bacteris. Per a fer-ho, s'utilitza un mètode híbrid que combina l'estimació de paràmetres Bayesiana i els desenvolupaments de caos polinomial generalitzat. / This work has been supported by the Spanish Ministerio de Economía y Competitividad grant MTM2017–89664–P. / Calatayud Gregori, J. (2020). Computational methods for random differential equations: probability density function and estimation of the parameters [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/138396 / TESIS / Premios Extraordinarios de tesis doctorales