<p dir="ltr">Statistical arbitrage is a prevalent trading strategy which takes advantage of mean reverse property of spreads constructed from pairs or portfolios of assets. Utilizing statistical models and algorithms, statistical arbitrage exploits and capitalizes on the pricing inefficiencies between securities or within asset portfolios. </p><p dir="ltr">In chapter 2, We propose a framework for constructing diversified portfolios with multiple pairs trading strategies. In our approach, several pairs of co-moving assets are traded simultaneously, and capital is dynamically allocated among different pairs based on the statistical characteristics of the historical spreads. This allows us to further consider various portfolio designs and rebalancing strategies. Working with empirical data, our experiments suggest the significant benefits of diversification within our proposed framework.</p><p dir="ltr">In chapter 3, we explore an optimal timing strategy for the trading of price spreads exhibiting mean-reverting characteristics. A sequential optimal stopping framework is formulated to analyze the optimal timings for both entering and subsequently liquidating positions, all while considering the impact of transaction costs. Then we leverages a refined signature optimal stopping method to resolve this sequential optimal stopping problem, thereby unveiling the precise entry and exit timings that maximize gains. Our framework operates without any predefined assumptions regarding the dynamics of the underlying mean-reverting spreads, offering adaptability to diverse scenarios. Numerical results are provided to demonstrate its superior performance when comparing with conventional mean reversion trading rules.</p><p dir="ltr">In chapter 4, we introduce an innovative model-free and reinforcement learning based framework for statistical arbitrage. For the construction of mean reversion spreads, we establish an empirical reversion time metric and optimize asset coefficients by minimizing this empirical mean reversion time. In the trading phase, we employ a reinforcement learning framework to identify the optimal mean reversion strategy. Diverging from traditional mean reversion strategies that primarily focus on price deviations from a long-term mean, our methodology creatively constructs the state space to encapsulate the recent trends in price movements. Additionally, the reward function is carefully tailored to reflect the unique characteristics of mean reversion trading.</p>

Diese Dissertation behandelt Fragen aus der stochastischen Analysis und der Finanzmathematik, die sich unter dem Begriff der Robustheit zusammenfassen lassen. Zunächst betrachten wir finanzmathematische Modelle mit Arbitragemöglichkeiten. Wir identifizieren die Abwesenheit von Arbitragemöglichkeiten der ersten Art (NA1) als minimale Eigenschaft, die in jedem finanzmathematischen Modell gelten muss, und zeigen, dass (NA1) äquivalent zur Existenz eines dominierenden lokalen Martingalmaßes ist. Als Beispiel für Prozesse, die (NA1) erfüllen, studieren wir stetige lokale Martingale, die darauf bedingt werden nie Null zu treffen. Anschließend verwenden wir eine modellfreie Version der (NA1) Eigenschaft, die es erlaubt, qualitative Eigenschaften von “typischen Preistrajektorien” zu beschreiben. Hier konstruieren wir ein pfadweises Itô-Integral. Dies deutet an, dass sich typische Preispfade als rough-path-Integratoren verwenden lassen. Nun entwickeln wir mittels Fourierentwicklungen einen alternativen Zugang zur rough-path-Theorie. Wir zerlegen das Integral in drei Operatoren mit verschiedenen Eigenschaften. So wird offensichtlich, dass Integratoren mit der Regularität der Brownschen Bewegung mit ihrer Lévy-Fläche versehen werden müssen, um ein pfadweise stetiges Integral zu erhalten. Daraufhin bemerken wir, dass die Integration zweier Funktionen gegeneinander äquivalent dazu ist, eine Funktion mit der Ableitung einer anderen (im Allgemeinen eine Distribution) zu multiplizieren. In höheren Dimensionen ist das Multiplikationsproblem jedoch allgemeiner. Wir verwenden Littlewood-Paley-Theorie, um unseren Fourier-Zugang zur rough-path-Theorie auf Funktionen mehrdimensionaler Variablen zu erweitern. Wir konstruieren einen Operator, der für Funktionen mit dem punktweisen Produkt übereinstimmt und in einer geeigneten Topologie stetig ist. Nun lassen sich stochastische partielle Differentialgleichungen lösen, die bisher aufgrund von Nichtlinearitäten nicht zugänglich waren. / This thesis deals with various problems from stochastic analysis and from mathematical finance that can best be summarized under the common theme of robustness. We begin by studying financial market models with arbitrage opportunities. We identify the weak notion of absence of arbitrage opportunities of the first kind (NA1) as the minimal property that every sensible asset price model should satisfy, and we prove that (NA1) is equivalent to the existence of a dominating local martingale. As examples of processes that satisfy (NA1) but do not admit equivalent local martingale measures, we study continuous local martingales conditioned not to hit zero. We continue by working with a model free formulation of the (NA1) property, which permits to describe qualitative properties of “typical asset price trajectories”. We construct a pathwise Itô integral for typical price paths. Our results indicate that typical price paths can be used as integrators in the theory of rough paths. Next, we use a Fourier series expansion to develop an alternative approach to rough path integration. We decompose the integral into three components with different behavior. Then it is easy to see that integrators with the regularity of the Brownian motion must be equipped with their Lévy area to obtain a pathwise continuous integral operator. We now note that integrating two functions against each other is equivalent to multiplying one with the derivative of the other, which will in general only be a distribution. In higher index dimensions however, the multiplication problem is more general. We use Littlewood-Paley theory to extend our Fourier approach from rough path integrals to multiplying functions of a multidimensional index. We construct an operator which agrees with the usual product for smooth functions, and which is continuous in a suitable topology. We apply this to solve stochastic partial differential equations that were previously difficult to access due to nonlinearities.

In der vorliegenden Arbeit werden Musterbildungsphänomene auf Submikrometerskalen in reaktiven Adsorbaten auf einkristallinen Katalysatoroberflächen theoretisch untersucht. Da auf solch kleinen Skalen Fluktuationen nicht mehr vernachlässigt werden können, wird eine mesoskopische Theorie entwickelt, die zwischen mikroskopischen Gittermodellen und Reaktions-Diffusions-Systemen vermittelt. Sie beschreibt die Dynamik lokal gemittelter Adsorbatbedeckungen im Rahmen eines Kontinuumsmodells unter Berücksichtigung interner Fluktuationen. Dieser Ansatz wird auf verschiedene Systeme angewendet, in denen sich Muster auf Längenskalen ausbilden, die kleiner als die charakterist ische Diffusionslänge sind, die typischerweise im Mikrometerbereich liegt. Wie beispielsweise in kürzlich durchgefh hrten Experimenten mit einem vergleichsweise schnellen Rastertunnelmikroskop beobachtet wurde, können attraktive Adsorbat-Adsorbat-Wech sel wirkungen zu verschiedenen Mustern auf Nanometerskalen führen. Hier wird zunächst eine einzelne Adsorbatspezies betrachtet. In Abwesenheit von Nichtgleichgewichtsreaktionen können hinreichend starke attraktive laterale Adsorbatwechselwirkungen einen Phasenh bergang erster Ordnung in der Adsorbatbedeckung induzieren. Die mesoskopische Entwicklungsgleichung wird auf die Modellierung der Kinetik dieses Phasenh bergangs angewendet. Berücksichtigt man zusätzlich eine Nichtgleichgewichtsreakti on, so können sich stationäre räumlich periodische Mikrostrukturen aufgrund der Konkurrenz zwischen dem Phasenh bergang und der Reaktion ausbilden. Die Vorraussetzungen für deren Auftreten und ihre charakteristischen Eigenschaften werden hier detailliert analysiert. Unter anderem werden alternierende Wechselwirkungen diskutiert und der Einfluß globaler Kopplung durch die Gasphase auf die Musterbildung wird betrachtet. Außerdem wird gezeigt, da8 die Mikrostrukturen auch durch vergleichsweise starke interne Fluktuationen nicht zerstört werden. Im nächsten Schritt wird ein hypothetisches Modell für zwei verschiedene Adsorbatspezies untersucht, in dem ein ähnlicher Mechanismus zur Bildung von laufenden und stehenden Wellenmustern auf der Nanoskala führt. Werden vergleichsweise starke interne Fluktuationen berücksichtigt, so brechen diese Wellenmuster auf und man beobachtet eine komplexe Dynamik miteinander wechselwirkender Wellenfragmente. Im letzten Beispiel wird anhand der Analyse eines einfachen Modells gezeigt, da8 sich auf Skalen unterhalb der Diffusionslänge selbstorganisierte Mikroreaktoren in einer einzelnen reaktiven Adsorbatspezies ausbilden können, ohne daß die Teilchen miteinander wechselwirken. Sie entsprechen lokalisierten Strukturen, die aufgrund des Zusammenspiels einer Nichtgleichgewichtsreaktion, der Diffusion und eines adsorbatinduzierten strukturellen Phasenh bergangs in der Substratoberfläche entstehen. / Nanoscale pattern formation in reactive adsorbates on single crystal surfaces is investigated theoretically. Because on such small scales fluctuations become important, a mesoscopic theory for the adsorbate coverage is developed, which aims at providing a link between microscopic lattice models and reaction-diffusion equations. It describes the dynamics for the locally averaged adsorbate coverages in a continuum model taking into account internal fluctuations. This approach is applied to several systems, where patterns on scales smaller than the characteristic diffusion length, which typically lies in the micrometer range, can be formed. As has been observed e.g. in recent experiments with fast scanning tunneling microscopy, a variety of nanoscale patterns can result from the presence of attractive adsorbate-adsorbate interactions. Here, at first a single species of such an adsorbate is considered. In the absence of nonequilibrium reactions, strong enough attractive lateral interactions can induce a first-order phase transition in the adsorbate coverage. The mesoscopic evolution equation is applied to model the kinetics of this phase transition. If additionally a nonequilibrium reaction is present, stationary spatially periodic microstructures may arise as a result of the competition of the attractive lateral interactions and the reactions. The conditions for their appearance and their properties are investigated in detail, e.g. alternating lateral interactions are discussed and the influence of global coupling through the gas phase is analyzed. Furthermore, it is shown that they are not destroyed by relatively strong internal fluctuations. In the next step, a hypothetical model for two different reactive adsorbate species is investigated, where a similar mechanism leads to the formation of nanoscale traveling and standing waves. In the presence of relatively strong internal fluctuations these waves break up and a complex dynamics of interacting wave fragments is observed. In the last example, it is shown in the analysis of a simple model that self-organized nonequilibrium microreactors with submicrometer sizes may spontaneously develop in a single reactive adsorbate species without attractive lateral interactions. They represent localized structures resulting from the interplay between reaction, diffusion and an adsorbate-induced structural transformation of the surface.

In dieser Arbeit untersuchen wir Superlösungen stochastischer Rückwärtsdifferentialgleichungen (BSDEs) und ein Gleichgewichtsmodell angewandt auf zwei spezifische verallgemeinerte Capital Asset Pricing Models (CAPMs). Unter der Annahme, dass Generatoren der BSDEs unterhalbstetig und von unten durch eine affine Funktion der Kontrollvariablen beschränkt sind sowie eine spezifische Normalisierungseigenschaft erfüllen, beweisen wir Existenz und Eindeutigkeit der minimalen Superlösung, wobei wir Semimartingalkonvergenz und eine geeignet definierte Präorder in Verbindung mit dem Zornschen Lemma nutzen. Anschließend betrachten wir konvexe Generatoren und restringieren admissible Kontrollen auf stetige Semimartingale, wobei wir eine Abhängigkeit des Generators von den Zerlegungsteilen zulassen. Wir beweisen Existenz von Superlösungen, die an endlich vielen Zeitpunkten minimal sind. Neben Stabilitätsresultaten für den nichtlinearen Operator, der einer Endbedingung den Wert der minimalen Superlösung zum Zeitpunk null zuordnet, leiten wir dessen duale Darstellung her und geben eine explizite Form dieser im Falle eines quadratischen Generators an. Ferner geben wir mittels der Dualität Bedingungen für die Existenz von Lösungen unter Nebenbedingungen. Im zweiten Teil der Arbeit behandeln wir ein Gleichgewichtsmodell in stetiger Zeit für verallgemeinerte CAPMs, das endlich viele Agenten und Finanzprodukte umfasst. Die Agenten maximieren exponentielle Nutzenfunktionen und ihre Anfangsausstattung wird von den gehandelten Produkten aufgespannt. Wir zeigen Existenz eines Gleichgewichts, in welchem die optimalen Handelsstrategien konstant sind und von jeweiliger Risikoaversion und Anfangsausstattung abhängen. Hiernach werden affine Prozesse sowie die Theorie des sogenannten Information-based Asset Pricing zur Modellierung herangezogen. Wir leiten semi-explizite Preisformeln her, die sich für effiziente numerische Berechnungen eignen, da keine Monte-Carlo-Methoden gebraucht werden. / In this thesis we study supersolutions of backward stochastic differential equations (BSDEs) and equilibrium pricing within two specific generalized capital asset pricing models (CAPMs). In the first part of the thesis we begin by assuming that the generators of the BSDEs under consideration are jointly lower semicontinuous, bounded from below by an affine function of the control variable, and satisfy a specific normalization property. We prove the existence and uniqueness of the minimal supersolution making use of a particular kind of semimartingale convergence and a suitably defined preorder in combination with Zorn''s lemma. Next, we assume generators to be convex and introduce constraints by restricting admissible controls to continuous semimartingales, where we allow for a dependence of the generator on the respective decomposition parts. We prove existence of supersolutions that are minimal at finitely many fixed times. Besides providing stability results for the non-linear operator that maps a terminal condition to the value of the minimal supersolution at time zero, we give a dual representation of it, including an explicit computation of the conjugate in the case of a quadratic generator, and derive conditions for the existence of solutions under constraints by means of the duality results. In the second part of the thesis we study equilibrium pricing in continuous time within generalized CAPMs. Our model comprises finitely many economic agents and tradable securities. The agents seek to maximize exponential utilities and their endowments are spanned by the securities. We show that an equilibrium exists and the agents'' optimal trading strategies are constant and dependent on their risk aversion and endowment. Affine processes, and the theory of information-based asset pricing are then used for modeling purposes. We derive semi-explicit pricing formulae which lend themselves to efficient numerical computations, as no Monte Carlo methods are needed.

Diese Dissertation widmet sich der Untersuchung singulärer stochastischer partieller Differentialgleichungen (engl. SPDEs). Wir entwickeln Erweiterungen der bisherigen Lösungstheorien, zeigen fundamentale Beziehungen zwischen verschiedenen Ansätzen und präsentieren Anwendungen in der Finanzmathematik und der mathematischen Physik. Die Theorie parakontrollierter Systeme wird für diskrete Räume formuliert und eine schwache Universalität für das parabolische Anderson Modell bewiesen. Eine fundamentale Relation zwischen Hairer's modellierten Distributionen und Paraprodukten wird bewiesen: Wir zeigen das sich der Raum modellierter Distributionen durch Paraprodukte beschreiben lässt. Dieses Resultat verallgemeinert die Fourierbeschreibung von Hölderräumen mittels Littlewood-Paley Theorie. Schließlich wird die Existenz von Lösungen der stochastischen Schrödingergleichung auf dem ganzen Raum bewiesen und eine Anwendung Hairer's Theorie zur Preisermittlung von Optionen aufgezeigt. / This thesis is concerned with the study of singular stochastic partial differential equations (SPDEs). We develop extensions to existing solution theories, present fundamental interconnections between different approaches and give applications in financial mathematics and mathematical physics. The theory of paracontrolled distribution is formulated for discrete systems, which allows us to prove a weak universality result for the parabolic Anderson model. This thesis further shows a fundamental relation between Hairer's modelled distributions and paraproducts: The space of modelled distributions can be characterized completely by using paraproducts. This can be seen a generalization of the Fourier description of Hölder spaces. Finally, we prove the existence of solutions to the stochastic Schrödinger equation on the full space and provide an application of Hairer's theory to option pricing.

The theoretical study of nonlinear expectations is the focus of attention for applications in a variety of different fields — often with the objective to model systems under incomplete information. Especially in mathematical finance, advances in the theory of sublinear expectations (also referred to as coherent risk measures) lay the theoretical foundation for modern approaches to evaluations under the presence of Knightian uncertainty. In this book, we introduce and study a large class of jump-type processes for sublinear expectations, which can be interpreted as Lévy-type processes under uncertainty in their characteristics. Moreover, we establish an existence and uniqueness theory for related nonlinear, nonlocal Hamilton-Jacobi-Bellman equations with non-dominated jump terms.

Analyzing the structure of a dataset is a challenging problem in high-dimensions as the volume of the space increases at an exponential rate and typically, data becomes sparse in this high-dimensional space. This poses a significant challenge to machine learning methods which rely on exploiting structures underlying data to make meaningful inferences. This dissertation proposes the <i>n</i>-TARP method as a building block for high-dimensional data analysis, in both supervised and unsupervised scenarios.<div><br></div><div>The basic element, <i>n</i>-TARP, consists of a random projection framework to transform high-dimensional data to one-dimensional data in a manner that yields point separations in the projected space. The point separation can be tuned to reflect classes in supervised scenarios and clusters in unsupervised scenarios. The <i>n</i>-TARP method finds linear separations in high-dimensional data. This basic unit can be used repeatedly to find a variety of structures. It can be arranged in a hierarchical structure like a tree, which increases the model complexity, flexibility and discriminating power. Feature space extensions combined with <i>n</i>-TARP can also be used to investigate non-linear separations in high-dimensional data.<br></div><div><br></div><div>The application of <i>n</i>-TARP to both supervised and unsupervised problems is investigated in this dissertation. In the supervised scenario, a sequence of <i>n</i>-TARP based classifiers with increasing complexity is considered. The point separations are measured by classification metrics like accuracy, Gini impurity or entropy. The performance of these classifiers on image classification tasks is studied. This study provides an interesting insight into the working of classification methods. The sequence of <i>n</i>-TARP classifiers yields benchmark curves that put in context the accuracy and complexity of other classification methods for a given dataset. The benchmark curves are parameterized by classification error and computational cost to define a benchmarking plane. This framework splits this plane into regions of "positive-gain" and "negative-gain" which provide context for the performance and effectiveness of other classification methods. The asymptotes of benchmark curves are shown to be optimal (i.e. at Bayes Error) in some cases (Theorem 2.5.2).<br></div><div><br></div><div>In the unsupervised scenario, the <i>n</i>-TARP method highlights the existence of many different clustering structures in a dataset. However, not all structures present are statistically meaningful. This issue is amplified when the dataset is small, as random events may yield sample sets that exhibit separations that are not present in the distribution of the data. Thus, statistical validation is an important step in data analysis, especially in high-dimensions. However, in order to statistically validate results, often an exponentially increasing number of data samples are required as the dimensions increase. The proposed <i>n</i>-TARP method circumvents this challenge by evaluating statistical significance in the one-dimensional space of data projections. The <i>n</i>-TARP framework also results in several different statistically valid instances of point separation into clusters, as opposed to a unique "best" separation, which leads to a distribution of clusters induced by the random projection process.<br></div><div><br></div><div>The distributions of clusters resulting from <i>n</i>-TARP are studied. This dissertation focuses on small sample high-dimensional problems. A large number of distinct clusters are found, which are statistically validated. The distribution of clusters is studied as the dimensionality of the problem evolves through the extension of the feature space using monomial terms of increasing degree in the original features, which corresponds to investigating non-linear point separations in the projection space.<br></div><div><br></div><div>A statistical framework is introduced to detect patterns of dependence between the clusters formed with the features (predictors) and a chosen outcome (response) in the data that is not used by the clustering method. This framework is designed to detect the existence of a relationship between the predictors and response. This framework can also serve as an alternative cluster validation tool.<br></div><div><br></div><div>The concepts and methods developed in this dissertation are applied to a real world data analysis problem in Engineering Education. Specifically, engineering students' Habits of Mind are analyzed. The data at hand is qualitative, in the form of text, equations and figures. To use the <i>n</i>-TARP based analysis method, the source data must be transformed into quantitative data (vectors). This is done by modeling it as a random process based on the theoretical framework defined by a rubric. Since the number of students is small, this problem falls into the small sample high-dimensions scenario. The <i>n</i>-TARP clustering method is used to find groups within this data in a statistically valid manner. The resulting clusters are analyzed in the context of education to determine what is represented by the identified clusters. The dependence of student performance indicators like the course grade on the clusters formed with <i>n</i>-TARP are studied in the pattern dependence framework, and the observed effect is statistically validated. The data obtained suggests the presence of a large variety of different patterns of Habits of Mind among students, many of which are associated with significant grade differences. In particular, the course grade is found to be dependent on at least two Habits of Mind: "computation and estimation" and "values and attitudes."<br></div>

This thesis work describes how to apply the stochastic analysis framework, presented in [1] for general priority-driven periodic real-time systems. The proposed framework is applicable to compute the response time distribution, the worst-case response time, and the deadline miss probability of the task under analysis in the fixed-priority driven scheduling system. To be specific, we modeled the task execution time by using the beta distribution. Moreover, we have evaluated the existing stochastic framework on a wide range of periodic systems with the help of defined evaluation parameters. In addition we have refined the notations used in system model and also developed new mathematics in order to facilitate the understanding with the concept. We have also introduced new concepts to obtain and validate the exact probabilistic task response time distribution.    Another contribution of this thesis is that we have extended the existing system model in order to deal with stochastic release time of a job. Moreover, a new algorithm is developed and validated using our extended framework where the stochastic dependencies exist due to stochastic release time patterns. / This is Second Version of the report. Submitted after few modifications made on the order of Thomas Nolte (Thesis Examiner). / START - Stochastic Real-Time Analysis of Embedded Software Systems

A main thrust of this thesis is to develop and explore linearization-based numeric-analytic integration techniques in the context of stochastically driven nonlinear oscillators of relevance in structural dynamics. Unfortunately, unlike the case of deterministic oscillators, available numerical or numeric-analytic integration schemes for stochastically driven oscillators, often modelled through stochastic differential equations (SDE-s), have significantly poorer numerical accuracy. These schemes are generally derived through stochastic Taylor expansions and the limited accuracy results from difficulties in evaluating the multiple stochastic integrals. We propose a few higher-order methods based on the stochastic version of transversal linearization and another method of linearizing the nonlinear drift field based on a Girsanov change of measures. When these schemes are implemented within a Monte Carlo framework for computing the response statistics, one typically needs repeated simulations over a large ensemble. The statistical error due to the finiteness of the ensemble (of size N, say)is of order 1/√N, which implies a rather slow convergence as N→∞. Given the prohibitively large computational cost as N increases, a variance reduction strategy that enables computing accurate response statistics for small N is considered useful. This leads us to propose a weak variance reduction strategy. Finally, we use the explicit derivative-free linearization techniques for state and parameter estimations for structural systems using the extended Kalman filter (EKF). A two-stage version of the EKF (2-EKF) is also proposed so as to account for errors due to linearization and unmodelled dynamics. In Chapter 2, we develop higher order locally transversal linearization (LTL) techniques for strong and weak solutions of stochastically driven nonlinear oscillators. For developing the higher-order methods, we expand the non-linear drift and multiplicative diffusion fields based on backward Euler and Newmark expansions while simultaneously satisfying the original vector field at the forward time instant where we intend to find the discretized solution. Since the non-linear vector fields are conditioned on the solution we wish to determine, the methods are implicit. We also report explicit versions of such linearization schemes via simple modifications. Local error estimates are provided for weak solutions. Weak linearized solutions enable faster computation vis-à-vis their strong counterparts. In Chapter 3, we propose another weak linearization method for non-linear oscillators under stochastic excitations based on Girsanov transformation of measures. Here, the non-linear drift vector is appropriately linearized such that the resulting SDE is analytically solvable. In order to account for the error in replacing of non-linear drift terms, the linearized solutions are multiplied by scalar weighting function. The weighting function is the solution of a scalar SDE(i.e.,Radon-Nikodym derivative). Apart from numerically illustrating the method through applications to non-linear oscillators, we also use the Girsanov transformation of measures to correct the truncation errors in lower order discretizations. In order to achieve efficiency in the computation of response statistics via Monte Carlo simulation, we propose in Chapter 4 a weak variance reduction strategy such that the ensemble size is significantly reduced without seriously affecting the accuracy of the predicted expectations of any smooth function of the response vector. The basis of the variance reduction strategy is to appropriately augment the governing system equations and then weakly replace the associated stochastic forcing functions through variance-reduced functions. In the process, the additional computational cost due to system augmentation is generally far less besides the accrued advantages due to a drastically reduced ensemble size. The variance reduction scheme is illustrated through applications to several non-linear oscillators, including a 3-DOF system. Finally, in Chapter 5, we exploit the explicit forms of the LTL techniques for state and parameters estimations of non-linear oscillators of engineering interest using a novel derivative-free EKF and a 2-EKF. In the derivative-free EKF, we use one-term, Euler and Newmark replacements for linearizations of the non-linear drift terms. In the 2-EKF, we use bias terms to account for errors due to lower order linearization and unmodelled dynamics in the mathematical model. Numerical studies establish the relative advantages of EKF-DLL as well as 2-EKF over the conventional forms of EKF. The thesis is concluded in Chapter 6 with an overall summary of the contributions made and suggestions for future research.

L’analyse dynamique des structures de grandes dimensions incluant de nombreux paramètres incertains et des non-linéarités localisées ou réparties peut être numériquement prohibitive. Afin de surmonter ce problème, des modèles d’approximation peuvent être développés pour reproduire avec précision et à faible coût de calcul la réponse de la structure.L’objectif de la première partie de ce mémoire est de développer des modèles numériques robustes vis-à-vis des modifications structurales (non-linéarités localisées, perturbations ou incertitudes paramétriques) et « légers » au sens de la réduction de la taille. Ces modèles sont construits, selon les approches de condensation directe et par synthèse modale, en enrichissant des bases de réduction tronquées, modale et de Craig-Bampton respectivement, avec des résidus statiques prenant compte des modifications structurales. Pour propager les incertitudes, l’accent est mis particulièrement sur la méthode du chaos polynomial généralisé. Sa combinaison avec les modèles réduits ainsi obtenus permet de créer des métamodèles mono et bi-niveaux, respectivement. Les deux métamodèles proposés sont comparés à d’autres métamodèles basés sur les méthodes du chaos polynomial généralisé et du Latin Hypercube appliquées sur des modèles complets et réduits. Les métamodèles proposés permettent d’approximer les comportements structuraux avec un coût de calcul raisonnable et sans perte significative de précision.La deuxième partie de ce mémoire est consacrée à l’analyse dynamique des structures périodiques non-linéaires en présence des imperfections : perturbations des paramètres structuraux ou incertitudes paramétriques. Deux études : déterministe ou stochastique, respectivement, sont donc menées. Pour ces deux configurations, un modèle analytique discret générique est proposé. Il consiste à appliquer la méthode des échelles multiples et la méthode de perturbation pour résoudre l’équation de mouvement et de projecter la solution obtenue sur des modes d’ondes stationnaires. Le modèle proposé conduit à un ensemble d’équations algébriques complexes couplées, fonctions du nombre et des positions des imperfections dans la structure. La propagation des incertitudes à travers le modèle ainsi construit est finalement assurée par les méthodes du Latin Hypercube et du chaos polynomial généralisé. La robustesse de la dynamique collective vis-à-vis des imperfections est étudiée à travers l’analyse statistique de la dispersion des réponses fréquentielles et des bassins d’attraction dans le domaine de multistabilité. L’étude numérique montre que la présence des imperfections dans une structure périodique renforce sa non-linéarité, élargit son domaine de multistabilité et génère une multiplicité de branches multimodale. / Dynamic analysis of large scale structures including several uncertain parameters and localized or distributed nonlinearities may be computationally unaffordable. In order to overcome this issue, approximation models can be developed to reproduce accurately the structural response at a low computational cost.The purpose of the first part of this thesis is to develop numerical models which must be robust against structural modifications (localized nonlinearities, parametric uncertainties or perturbations) and reduce the size of the initial problem. These models are created, according to the direct condensation and the component mode synthesis, by enriching truncated reduction modal bases and Craig-Bampton transformations, respectively, with static residual vectors accounting for the structural modifications. To propagate uncertainties through these first-level and second-level reduced order models, respectively, we focus particularly on the generalized polynomial chaos method. This methods combination allows creating first-level and second-level metamodels, respectively. The two proposed metamodels are compared to other metamodels based on the polynomial chaos method and Latin Hypercube method applied on reduced and full models. The proposed metamodels allow approximating the structural behavior at a low computational cost without a significant loss of accuracy.The second part of this thesis is devoted to the dynamic analysis of nonlinear periodic structures in presence of imperfections: parametric perturbations or uncertainties. Deterministic or stochastic analyses, respectively, are therefore carried out. For both configurations, a generic discrete analytical model is proposed. It consists in applying the multiple scales method and the perturbation theory to solve the equation of motion and then on projecting the resulting solution on standing wave modes. The proposed model leads to a set of coupled complex algebraic equations, depending on the number and positions of imperfections in the structure. Uncertainty propagation through the proposed model is finally done using the Latin Hypercube method and the generalized polynomial chaos expansion. The robustness the collective dynamics against imperfections is studied through statistical analysis of the frequency responses and the basins of attraction dispersions in the multistability domain. Numerical results show that the presence of imperfections in a periodic structure strengthens its nonlinearity, expands its multistability domain and generates a multiplicity of multimodal branches.