[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

Cette thèse porte sur l'étude des dynamiques spatiales et évolutives d'une population à l'aide d'outils probabilistes et déterministes. Dans la première partie, nous cherchons à comprendre l'effet de l'hétérogénéité de l'environnement sur l'évolution des espèces. La population considérée est modélisée par un processus individu-centré avec interactions qui décrit les événements de naissances, morts, mutations et diffusions spatiales de chaque individu. Les taux des événements dépendent des caractéristiques des individus : traits phénotypes et positions spatiales. Dans un premier temps, nous étudions le système d'équations aux dérivées partielles qui décrit la dynamique spatiale et démographique d'une population composée de deux traits dans une limite grande population. Nous caractérisons précisément les conditions d'extinction et de survie en temps long de cette population. Dans un deuxième temps, nous étudions le modèle individuel initial sous deux asymptotiques : grande population et mutations rares de telle sorte que les échelles de temps démographiques et mutationnelles sont séparées. Ainsi, lorsqu'un mutant apparaît, la population résidente est à l'équilibre démographique. Nous cherchons alors à caractériser la probabilité de survie de la population issue de ce mutant. Puis, en étudiantle processus dans l'échelle des mutations, nous prouvons que le processus individu-centré converge vers un processus de sauts qui décrit les fixations successives des traits les plus avantagés ainsi que la répartition spatiale des populations portant ces traits. Nous généralisons ensuite le modèle pour introduire des interactions de type mutualiste entre deux espèces. Nous étudions ce modèle dans une limite de grande population. Nous donnons par ailleurs des résultats numériques et une analyse biologique détaillée des comportements obtenus autour de deux problématiques : la coévolution de niches spatiales et phénotypiques d'espèces en interaction mutualiste et les dynamiques d'invasions d'un espace homogène par des espèces mutualistes. Dans la deuxième partie de cette thèse, nous développons un modèle probabiliste pour étudier finement l'effet d'une préférence sexuelle sur la spéciation. La population est ici structurée sur deux patchs et les individus, caractérisés par un trait, sont écologiquement et démographiquement équivalents et se distinguent uniquement par leur préférence sexuelle: deux individus de même trait ont plus de chance de se reproduire que deux individus de traits distincts. Nous montrons qu'en l'absence de toute autre différence écologique, la préférence sexuelle mène à un isolement reproductif entre les deux patchs. / We study the spatial and evolutionary dynamics of a population by using probabilistic and deterministic tools. In the first part of this thesis, we are concerned with the influence of a heterogeneous environment on the evolution of species. The population is modeled by an individual-based process with some interactions and which describes the birth, the death, the mutation and the spatial diffusion of each individual. The rates of those events depend on the characteristics of the individuals : their phenotypic trait and their spatial location. First, we study the system of partial differential equations that describes the spatial and demographic dynamics of a population composed of two traits in a large population limit. We characterize precisely the conditions of extinction and long time survival for this population. Secondly, we study the initial individual-based model under two asymptotic: large population and rare mutations such as demographic and mutational timescales are separated. Thus, when a mutant appears, the resident population has reached its demographic balance. We characterize the survival probability of the population descended from this mutant. Then, by studyingthe process in the mutational scale, we prove that the microscopic process converges to a jump process which describes the successive fixations of the most advantaged traits and the spatial distribution of populations carrying these traits. We then extend the model to introduce mutualistic interactions between two species. We study this model in a limit of large population. We also give numerical results and a detailed biological behavior analysis around two issues: the co-evolution of phenotypic and spatial niches of mutualistic species and the invasion dynamics of a homogeneous space by these species. In the second part of this thesis, we develop a probabilistic model to study the effect of the sexual preference on the speciation. Here, the population is structured on two patches and the individuals, characterized by a trait, are ecologically and demographically similar and differ only in their sexual preferences: two individuals of the same trait are more likely to reproduce than two individuals of distinct traits. We show that in the absence of any other ecological differences, the sexual preferences lead to reproductive isolation between the two patches.

Using a Lie symmetry group generator and a generalized form of Manale's formula for solving second order ordinary di erential equations, we determine new symmetries for the one and two dimensional heat equations, leading to new solutions. As an application, we test a formula resulting from this approach on thin plate heat conduction. / Applied Mathematics / M.Sc. (Applied Mathematics)

This thesis develops efficient modeling frameworks via a Partial Differential Equation (PDE) approach for multi-factor financial derivatives, with emphasis on three-factor models, and studies highly efficient implementations of the numerical methods on novel high-performance computer architectures, with particular focus on Graphics Processing Units (GPUs) and multi-GPU platforms/clusters of GPUs. Two important classes of multi-factor financial instruments are considered: cross-currency/foreign exchange (FX) interest rate derivatives and multi-asset options. For cross-currency interest rate derivatives, the focus of the thesis is on Power Reverse Dual Currency (PRDC) swaps with three of the most popular exotic features, namely Bermudan cancelability, knockout, and FX Target Redemption. The modeling of PRDC swaps using one-factor Gaussian models for the domestic and foreign interest short rates, and a one-factor skew model for the spot FX rate results in a time-dependent parabolic PDE in three space dimensions. Our proposed PDE pricing framework is based on partitioning the pricing problem into several independent pricing subproblems over each time period of the swap's tenor structure, with possible communication at the end of the time period. Each of these subproblems requires a solution of the model PDE. We then develop a highly efficient GPU-based parallelization of the Alternating Direction Implicit (ADI) timestepping methods for solving the model PDE. To further handle the substantially increased computational requirements due to the exotic features, we extend the pricing procedures to multi-GPU platforms/clusters of GPUs to solve each of these independent subproblems on a separate GPU. Numerical results indicate that the proposed GPU-based parallel numerical methods are highly efficient and provide significant increase in performance over CPU-based methods when pricing PRDC swaps. An analysis of the impact of the FX volatility skew on the price of PRDC swaps is provided. In the second part of the thesis, we develop efficient pricing algorithms for multi-asset options under the Black-Scholes-Merton framework, with strong emphasis on multi-asset American options. Our proposed pricing approach is built upon a combination of (i) a discrete penalty approach for the linear complementarity problem arising due to the free boundary and (ii) a GPU-based parallel ADI Approximate Factorization technique for the solution of the linear algebraic system arising from each penalty iteration. A timestep size selector implemented efficiently on GPUs is used to further increase the efficiency of the methods. We demonstrate the efficiency and accuracy of the proposed GPU-based parallel numerical methods by pricing American options written on three assets.

This thesis is concerned with the mathematical analysis of electricity and carbon emission markets. We introduce a novel, versatile and tractable stochastic framework for the joint price formation of electricity spot prices and allowance certificates. In the proposed framework electricity and allowance prices are explained as functions of specific fundamental factors, such as the demand for electricity and the prices of the fuels used for its production. As a result, the proposed model very clearly captures the complex dependency of the modelled prices on the aforementioned fundamental factors. The allowance price is obtained as the solution to a coupled forward-backward stochastic differential equation. We provide a rigorous proof of the existence and uniqueness of a solution to this equation and analyse its behaviour using asymptotic techniques. The essence of the model for the electricity price is a carefully chosen and explicitly constructed function representing the supply curve in the electricity market. The model we propose accommodates most regulatory features that are commonly found in implementations of emissions trading systems and we analyse in detail the impact these features have on the prices of allowance certificates. Thereby we reveal a weakness in existing regulatory frameworks, which, in rare cases, can lead to allowance prices that do not conform with the conditions imposed by the regulator. We illustrate the applicability of our model to the pricing of derivative contracts, in particular clean spread options and numerically illustrate its ability to "see" relationships between the fundamental variables and the option contract, which are usually unobserved by other commonly used models in the literature. The results we obtain constitute flexible tools that help to efficiently evaluate the financial impact current or future implementations of emissions trading systems have on participants in these markets.

Les systèmes décrits par les équations aux dérivées partielles, appartiennent à la classe des systèmes dynamiques impliquant des fonctions dépendantes de plusieurs variables, comme le temps et l'espace. Déjà fortement répandus pour la modélisation mathématique de phénomènes physiques et environnementaux, ces systèmes ont un rôle croissant dans les domaines de l'automatique. Cette expansion, provoquée par les avancées technologiques au niveau des capteurs facilitant l'acquisition de données et par les nouveaux enjeux environnementaux, incite au développement de nouvelles thématiques de recherche. L'une de ces thématiques, est l'étude des problèmes inverses et plus particulièrement l'identification paramétrique des équations aux dérivées partielles. Tout abord, une description détaillée des différentes classes de systèmes décrits par ces équations est présentée puis les problèmes d'identification qui leur sont associés sont soulevés. L'accent est mis sur l'estimation paramétrique des équations linéaires, homogènes ou non, et sur les équations linéaires à paramètres variant. Un point commun à ces problèmes d'identification réside dans le caractère bruité et échantillonné des mesures de la sortie. Pour ce faire, deux types d'outils principaux ont été élaborés. Certaines techniques de discrétisation spatio-temporelle ont été utilisées pour faire face au caractère échantillonné des données; les méthodes de variable instrumentale, pour traiter le problème lié à la présence de bruit de mesure. Les performances de ces méthodes ont été évaluées selon des protocoles de simulation numérique reproduisant des situations réalistes de phénomènes physique et environnementaux, comme la diffusion de polluant dans une rivière / A large variety of natural, industrial, and environmental systems involves phenomena that are continuous functions not only of time, but also of other independent variables, such as space coordinates. Typical examples are transportation phenomena of mass or energy, such as heat transmission and/or exchange, humidity diffusion or concentration distributions. These systems are intrinsically distributed parameter systems whose description usually requires the introduction of partial differential equations. There is a significant number of phenomena that can be simulated and explained by partial differential equations. Unfortunately all phenomena are not likely to be represented by a single equation. Also, it is necessary to model the largest possible number of behaviors to consider several classes of partial differential equations. The most common are linear equations, but the most representative are non-linear equations. The nonlinear equations can be formulated in many different ways, the interest in nonlinear equations with linear parameters varying is studied. The aim of the thesis is to develop new estimators to identify the systems described by these partial differential equations. These estimators must be adapted with the actual data obtained in experiments. It is therefore necessary to develop estimators that provide convergent estimates when one is in the presence of missing data and are robust to measurement noise. In this thesis, identification methods are proposed for partial differential equation parameter estimation. These methods involve the introduction of estimators based on the instrumental variable technique

Using a Lie symmetry group generator and a generalized form of Manale's formula for solving second order ordinary di erential equations, we determine new symmetries for the one and two dimensional heat equations, leading to new solutions. As an application, we test a formula resulting from this approach on thin plate heat conduction. / Applied Mathematics / M. Sc. (Applied Mathematics)

Residual-distribution methods offer several potential benefits over classical methods, such as a means of applying upwinding in a multi-dimensional manner and a multi-dimensional positivity property. While it is apparent that residual-distribution methods also offer higher accuracy than finite-volume methods on similar meshes, few studies have directly compared the performance of the two approaches in a systematic and quantitative manner. In this study, comparisons between residual distribution and finite volume are made for steady-state smooth and discontinuous flows of gas dynamics, governed by hyperbolic conservation laws, to illustrate the strengths and deficiencies of the residual-distribution method. Deficiencies which reduce the accuracy are analyzed and a new nonlinear scheme is proposed that closely reproduces or surpasses the accuracy of the best linear residual-distribution scheme. The accuracy is further improved by extending the scheme to fourth order using established finite-element techniques. Finally, the compact stencil, arithmetic workload, and data parallelism of the fourth-order residual-distribution scheme are exploited to accelerate parallel computations on an architecture consisting of both CPU cores and a graphics processing unit. Numerical experiments are used to assess the gains to efficiency and possible monetary savings that may be provided by accelerated architectures.

Gas sensors are today used in many different application areas, and one growing future market is battery operated sensors. As many gas sensor components are heated, one major limit of the operation time is caused by the power dissipated as heat. AppliedSensor is a company that develops and produces gas sensor components, modules and solutions, among which battery operated gas sensors are one targeted market. The aim of the diploma work has been to simulate the heat transfer on a hydrogen gas sensor component and its closest surroundings consisting of a carrier mounted on a printed circuit board. The component is heated in order to improve the performance of the gas sensing element. Power dissipation occurs by all three modes of heat transfer; conduction from the component through bond wires and carrier to the printed circuit board as well as convection and radiation from all the surfaces. It is of interest to AppliedSensor to understand which factors influence the heat transfer. This knowledge will be used to improve different aspects of the gas sensor, such as the power consumption. Modeling and simulation have been performed in FEMLAB, a tool for solving partial differential equations by the finite element method. The sensor system has been defined by the geometry and the material properties of the objects. The system of partial differential equations, consisting of the heat equation describing conduction and boundary conditions specifying convection and radiation, was solved and the solution was validated against experimental data. The convection increases with the increase of hydrogen concentration. A great effort was made to finding a model for the convection. Two different approaches were taken, the first based on known theory from the area and the second on experimental data. When the first method was compared to experiments, it turned out that the theory was insufficient to describe this small system involving hydrogen, which was an unexpected but interesting result. The second method matched the experiments well. For the continuation of the project at the company, a better model of the convection would be a great improvement.

Residual-distribution methods offer several potential benefits over classical methods, such as a means of applying upwinding in a multi-dimensional manner and a multi-dimensional positivity property. While it is apparent that residual-distribution methods also offer higher accuracy than finite-volume methods on similar meshes, few studies have directly compared the performance of the two approaches in a systematic and quantitative manner. In this study, comparisons between residual distribution and finite volume are made for steady-state smooth and discontinuous flows of gas dynamics, governed by hyperbolic conservation laws, to illustrate the strengths and deficiencies of the residual-distribution method. Deficiencies which reduce the accuracy are analyzed and a new nonlinear scheme is proposed that closely reproduces or surpasses the accuracy of the best linear residual-distribution scheme. The accuracy is further improved by extending the scheme to fourth order using established finite-element techniques. Finally, the compact stencil, arithmetic workload, and data parallelism of the fourth-order residual-distribution scheme are exploited to accelerate parallel computations on an architecture consisting of both CPU cores and a graphics processing unit. Numerical experiments are used to assess the gains to efficiency and possible monetary savings that may be provided by accelerated architectures.