x, 110 p. : ill. A print copy of this thesis is available through the UO Libraries. Search the library catalog for the location and call number. / This dissertation examines the existence of the self-intersection local time for a superprocess over a stochastic flow in dimensions d ≤ 3, which through constructive methods, gives a Tanaka like representation. The superprocess over a stochastic flow is a superprocess with dependent spatial motion, and thus Dynkin's proof of existence, which requires multiplicity of the log-Laplace functional, no longer applies. Skoulakis and Adler's method of calculating moments is extended to higher moments, from which existence follows. / Committee in charge: Hao Wang, Co-Chairperson, Mathematics; David Levin, Co-Chairperson, Mathematics; Christopher Sinclair, Member, Mathematics; Huaxin Lin, Member, Mathematics; Van Kolpin, Outside Member, Economics

Identifying marine pollutant sources is essential in order to assess, contain and minimize their risk. We propose a Lagrangian Particle Tracking algorithm (LPT) to study the transport of passive tracers continuously released from fixed and moving sources and to identify their source in a backward mode. The LPT is designed to operate with uncertain flow fi elds, described by an ensemble of realizations of the sea currents. Starting from a region of high probability, re- verse tracking is used to generate inverse maps. A probability-weighted distance between the resulting inverse maps and the source trajectory is then minimized to identify the likely source of pollution. We conduct realistic simulations to demonstrate the efficiency of the proposed algorithm in the Mediterranean Sea using ocean data available from Copernicus Marine Environment Monitoring Services. Passive tracers are released along the path of a ship and propagated with an ensemble of flow fi elds forward in time to generate a probability map, which is then used for the inverse problem of source identi fication. Our experiments suggest that the algorithm is able to efficiently capture the release time and source, with some test cases successfully pinpointing the release time and source up to two weeks back in time.

Orientador: Pedro Jose Catuogno / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática, Estatística e Computação Científica / Made available in DSpace on 2018-08-17T23:03:18Z (GMT). No. of bitstreams: 1 ChipanaMollinedo_DavidAlexander_M.pdf: 748774 bytes, checksum: 2c70408db9f4fbab1d9846af4be3736b (MD5) Previous issue date: 2011 / Resumo: Neste trabalho estamos interessados em estudar o teorema limite para fluxos estocásticos. Apresentamos a teoria de semimartingales de H. Kunita e algumas noções relativas a fluxos de aplicações mensuráveis...Observação: O resumo, na íntegra, poderá ser visualizado no texto completo da tese digital / Abstract: In this work we are interested in studying the limit theorem for stochastic flows. We present the theory of semimartingales given by H. Kunita and some notions related to flow of measurable applications...Note: The complete abstract is available with the full electronic digital thesis or dissertation / Mestrado / Matematica / Mestre em Matemática

Orientador: Paulo Regis Caron Ruffino / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica / Made available in DSpace on 2018-08-12T16:41:27Z (GMT). No. of bitstreams: 1 Silva_FabianoBorgesda_D.pdf: 848923 bytes, checksum: 27f2cf2ad665ac271db23db385dab86f (MD5) Previous issue date: 2009 / Resumo: Este trabalho consiste basicamente em três níveis de decomposições de fluxos estocásticos: 1) decomposição via G-estruturas; 2) decomposição com componente em trajetórias hamiltonianas e 3) conjugações de fluxos aleatórios ¿Observação: O resumo, na íntegra poderá ser visualizado no texto completo da tese digital. / Abstract: This thesis concerns three different kind of decomposition of stochastic flows: 1) decompositions preserving G-structures; 2) decompositions with a component whose trajectories are hamiltonians and; 3) tensor preserving conjugacies with random time differentiable cociclos ...Note: The complete abstract is available with the full electronic digital thesis or dissertations. / Doutorado / Sistemas Dinamicos / Doutor em Matemática

Orientador: Paulo Regis Caron Ruffino / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação Científica / Made available in DSpace on 2018-08-27T10:29:03Z (GMT). No. of bitstreams: 1 Morgado_LeandroBatista_D.pdf: 1320837 bytes, checksum: db1015f01556b3de2b1f7ca1c6bf33d3 (MD5) Previous issue date: 2015 / Resumo: Este trabalho aborda alguns aspectos da teoria de equações diferenciais estocásticas em relação a semimartingales com saltos, suas aplicações na decomposição de fluxos estocásticos em variedades, bem como algumas implicações de natureza geométrica. Inicialmente, em uma variedade munida de distribuições complementares, discutimos o problema da decomposição de fluxos estocásticos contínuos, isto é, gerados por EDE em relação ao movimento Browniano. Resultados anteriores garantem a existência de uma decomposição em difeomorfismos que preservam as distribuições até um tempo de parada. Usando a assim denominada equação de Marcus, bem como uma técnica que denominamos equação 'stop and go', vamos construir um fluxo estocástico próximo ao original, com a propriedade adicional que o fluxo construído pode ser decomposto além do tempo de parada inicial. Em seguida, trataremos da decomposição de fluxos estocásticos no caso descontínuo, isto é, para processos gerados por uma EDE em relação a um semimartingale com saltos. Após uma discussão sobre a existência da decomposição, obtemos as EDEs para as componentes respectivas, a partir de uma extensão que propomos da fórmula de Itô-Ventzel-Kunita. Finalmente, propomos um modelo de equações diferenciais estocásticas com retardo incluindo saltos. A ideia é modelar certos fenômenos em que a informação pode chegar ao receptor por diferentes canais: de forma contínua, mas com retardo, e em tempos discretos, de forma instantânea. Vamos abordar aspectos geométricos relacionados ao tema: transporte paralelo em curvas diferenciáveis com saltos, bem como possibilidade de levantamento de uma solução do nosso modelo de equação para o fibrado de bases de uma variedade diferenciável / Abstract: The main subject of this thesis is the theory of stochastic differential equations driven by semimartingales with jumps. We consider applications in the decomposition of stochastic flows in differentiable manifolds, and geometrical aspects about these equations. Initially, in a differentiable manifold endowed with a pair of complementary distributions, we discuss the decomposition of continuous stochastic flows, that is, flows generated by SDEs driven by Brownian motion. Previous results guarantee that, under some assumptions, there exists a decomposition in diffeomorphisms that preserves the distributions up to a stopping time. Using the so called Marcus equation, and a technique that we call 'stop and go' equation, we construct a stochastic flow close to the original one, with the property that the constructed flow can be decomposed further on the stopping time. After, we deal with the decomposition of stochastic flows in the discontinuous case, that is, processes generated by SDEs driven by semimartingales with jumps. We discuss the existence of this decomposition, and obtain the SDEs for the respective components, using an extension of the Itô-Ventzel-Kunita formula. Finally, we propose a model of stochastic differential equations including delay and jumps. The idea is to describe some phenomena such that the information comes to the receptor by different channels: continuously, with some delay, and in discrete times, instantaneously. We deal with geometrical aspects related with this subject: parallel transport in càdlàg curves, and lifting of solutions of these equations to the linear frame bundle of a differentiable manifold / Doutorado / Matematica / Doutor em Matemática

Recently, the environmental impact of aircraft and rising fuel prices have become an increasing concern in the aviation industry. To address these problems, organizations such as NASA have set demanding goals for reducing aircraft emissions, fuel burn, and noise. In an effort to reach the goals, a movement toward more-electric aircraft and electric propulsion has emerged. With this movement, the number of critical electrical loads on an aircraft is increasing causing power system reliability to be a point of concern. Currently, power system reliability is maintained through the use of back-up power supplies such as batteries and ram-air-turbines (RATs). However, the increasing power requirements for critical loads will quickly outgrow the capacity of the emergency devices. Therefore, reliability needs to be addressed when designing the primary power distribution system. Power system reliability is a function of component reliability and redundancy. Component reliability is often not determined until detailed component design has occurred; however, the amount of redundancy in the system is often set during the system architecting phase. In order to meet the capacity and reliability requirements of future power distribution systems, a method for redundancy allocation during the system architecting phase is needed. This thesis presents an aircraft power system design methodology that is based upon the engineering decision process. The methodology provides a redundancy allocation strategy and quantitative trade-off environment to compare architecture and technology combinations based upon system capacity, weight, and reliability criteria. The methodology is demonstrated by architecting the power distribution system of an aircraft using turboelectric propulsion. The first step in the process is determining the design criteria which includes a 40 MW capacity requirement, a 20 MW capacity requirement for the an engine-out scenario, and a maximum catastrophic failure rate of one failure per billion flight hours. The next step is determining gaps between the performance of current power distribution systems and the requirements of the turboelectric system. A baseline architecture is analyzed by sizing the system using the turboelectric system power requirements and by calculating reliability using a stochastic flow network. To overcome the deficiencies discovered, new technologies and architectures are considered. Global optimization methods are used to find technology and architecture combinations that meet the system objectives and requirements. Lastly, a dynamic modeling environment is constructed to study the performance and stability of the candidate architectures. The combination of the optimization process and dynamic modeling facilitates the selection of a power system architecture that meets the system requirements and objectives.

<div>Uncertainty quantification techniques based on the spectral approach have been studied extensively in the literature to characterize and quantify, at low computational cost, the impact that uncertainties may have on large-scale engineering problems. One such technique is the <i>generalized polynomial chaos</i> (gPC) which utilizes a time-independent orthogonal basis to expand a stochastic process in the space of random functions. The method uses a specific Askey-chaos system that is concordant with the measure defined in the probability space in order to ensure exponential convergence to the solution. For nearly two decades, this technique has been used widely by several researchers in the area of uncertainty quantification to solve stochastic problems using the spectral approach. However, a major drawback of the gPC method is that it cannot be used in the resolution of problems that feature strong nonlinear dependencies over the probability space as time progresses. Such downside arises due to the time-independent nature of the random basis, which has the undesirable property to lose unavoidably its optimality as soon as the probability distribution of the system's state starts to evolve dynamically in time.</div><div><br></div><div>Another technique is the <i>time-dependent generalized polynomial chaos</i> (TD-gPC) which utilizes a time-dependent orthogonal basis to better represent the stochastic part of the solution space (aka random function space or RFS) in time. The development of this technique was motivated by the fact that the probability distribution of the solution changes with time, which in turn requires that the random basis is frequently updated during the simulation to ensure that the mean-square error is kept orthogonal to the discretized RFS. Though this technique works well for problems that feature strong nonlinear dependencies over the probability space, the TD-gPC method possesses a serious issue: it suffers from the curse of dimensionality at the RFS level. This is because in all gPC-based methods the RFS is constructed using a tensor product of vector spaces with each of these representing a single RFS over one of the dimensions of the probability space. As a result, the higher the dimensionality of the probability space, the more vector spaces needed in the construction of a suitable RFS. To reduce the dimensionality of the RFS (and thus, its associated computational cost), gPC-based methods require the use of versatile sparse tensor products within their numerical schemes to alleviate to some extent the curse of dimensionality at the RFS level. Therefore, this curse of dimensionality in the TD-gPC method alludes to the need of developing a more compelling spectral method that can quantify uncertainties in long-time response of dynamical systems at much lower computational cost.</div><div><br></div><div>In this work, a novel numerical method based on the spectral approach is proposed to resolve the curse-of-dimensionality issue mentioned above. The method has been called the <i>flow-driven spectral chaos</i> (FSC) because it uses a novel concept called <i>enriched stochastic flow maps</i> to track the evolution of a finite-dimensional RFS efficiently in time. The enriched stochastic flow map does not only push the system's state forward in time (as would a traditional stochastic flow map) but also its first few time derivatives. The push is performed this way to allow the random basis to be constructed using the system's enriched state as a germ during the simulation and so as to guarantee exponential convergence to the solution. It is worth noting that this exponential convergence is achieved in the FSC method by using only a few number of random basis vectors, even when the dimensionality of the probability space is considerably high. This is for two reasons: (1) the cardinality of the random basis does not depend upon the dimensionality of the probability space, and (2) the cardinality is bounded from above by <i>M+n+1</i>, where <i>M</i> is the order of the stochastic flow map and <i>n</i> is the order of the governing stochastic ODE. The boundedness of the random basis from above is what makes the FSC method be curse-of-dimensionality free at the RFS level. For instance, for a dynamical system that is governed by a second-order stochastic ODE (<i>n=2</i>) and driven by a stochastic flow map of fourth-order (<i>M=4</i>), the maximum number of random basis vectors to consider within the FSC scheme is just 7, independent whether the dimensionality of the probability space is as low as 1 or as high as 10,000.</div><div><br></div><div>With the aim of reducing the complexity of the presentation, this dissertation includes three levels of abstraction for the FSC method, namely: a <i>specialized version</i> of the FSC method for dealing with structural dynamical systems subjected to uncertainties (Chapter 2), a <i>generalized version</i> of the FSC method for dealing with dynamical systems governed by (nonlinear) stochastic ODEs of arbitrary order (Chapter 3), and a <i>multi-element version</i> of the FSC method for dealing with dynamical systems that exhibit discontinuities over the probability space (Chapter 4). This dissertation also includes an implementation of the FSC method to address the dynamics of large-scale stochastic structural systems more effectively (Chapter 5). The implementation is done via a modal decomposition of the spatial function space as a means to reduce the number of degrees of freedom in the system substantially, and thus, save computational runtime.</div>

No description available.

L'objectif de cette thèse est l'étude de la représentation probabiliste des différentes classes d'EDPSs non-linéaires(semi-linéaires, complètement non-linéaires, réfléchies dans un domaine) en utilisant les équations différentielles doublement stochastiques rétrogrades (EDDSRs). Cette thèse contient quatre parties différentes. Nous traitons dans la première partie les EDDSRs du second ordre (2EDDSRs). Nous montrons l'existence et l'unicité des solutions des EDDSRs en utilisant des techniques de contrôle stochastique quasi- sure. La motivation principale de cette étude est la représentation probabiliste des EDPSs complètement non-linéaires. Dans la deuxième partie, nous étudions les solutions faibles de type Sobolev du problème d'obstacle pour les équations à dérivées partielles inteégro-différentielles (EDPIDs). Plus précisément, nous montrons la formule de Feynman-Kac pour l'EDPIDs par l'intermédiaire des équations différentielles stochastiques rétrogrades réfléchies avec sauts (EDSRRs). Plus précisément, nous établissons l'existence et l'unicité de la solution du problème d'obstacle, qui est considérée comme un couple constitué de la solution et de la mesure de réflexion. L'approche utilisée est basée sur les techniques de flots stochastiques développées dans Bally et Matoussi (2001) mais les preuves sont beaucoup plus techniques. Dans la troisième partie, nous traitons l'existence et l'unicité pour les EDDSRRs dans un domaine convexe D sans aucune condition de régularité sur la frontière. De plus, en utilisant l'approche basée sur les techniques du flot stochastiques nous démontrons l'interprétation probabiliste de la solution faible de type Sobolev d'une classe d'EDPSs réfléchies dans un domaine convexe via les EDDSRRs. Enfin, nous nous intéressons à la résolution numérique des EDDSRs à temps terminal aléatoire. La motivation principale est de donner une représentation probabiliste des solutions de Sobolev d'EDPSs semi-linéaires avec condition de Dirichlet nul au bord. Dans cette partie, nous étudions l'approximation forte de cette classe d'EDDSRs quand le temps terminal aléatoire est le premier temps de sortie d'une EDS d'un domaine cylindrique. Ainsi, nous donnons les bornes pour l'erreur d'approximation en temps discret. Cette partie se conclut par des tests numériques qui démontrent que cette approche est effective. / The objective of this thesis is to study the probabilistic representation (Feynman-Kac for- mula) of different classes ofStochastic Nonlinear PDEs (semilinear, fully nonlinear, reflected in a domain) by means of backward doubly stochastic differential equations (BDSDEs). This thesis contains four different parts. We deal in the first part with the second order BDS- DEs (2BDSDEs). We show the existence and uniqueness of solutions of 2BDSDEs using quasi sure stochastic control technics. The main motivation of this study is the probabilistic representation for solution of fully nonlinear SPDEs. First, under regularity assumptions on the coefficients, we give a Feynman-Kac formula for classical solution of fully nonlinear SPDEs and we generalize the work of Soner, Touzi and Zhang (2010-2012) for deterministic fully nonlinear PDE. Then, under weaker assumptions on the coefficients, we prove the probabilistic representation for stochastic viscosity solution of fully nonlinear SPDEs. In the second part, we study the Sobolev solution of obstacle problem for partial integro-differentialequations (PIDEs). Specifically, we show the Feynman-Kac formula for PIDEs via reflected backward stochastic differentialequations with jumps (BSDEs). Specifically, we establish the existence and uniqueness of the solution of the obstacle problem, which is regarded as a pair consisting of the solution and the measure of reflection. The approach is based on stochastic flow technics developed in Bally and Matoussi (2001) but the proofs are more technical. In the third part, we discuss the existence and uniqueness for RBDSDEs in a convex domain D without any regularity condition on the boundary. In addition, using the approach based on the technics of stochastic flow we provide the probabilistic interpretation of Sobolev solution of a class of reflected SPDEs in a convex domain via RBDSDEs. Finally, we are interested in the numerical solution of BDSDEs with random terminal time. The main motivation is to give a probabilistic representation of Sobolev solution of semilinear SPDEs with Dirichlet null condition. In this part, we study the strong approximation of this class of BDSDEs when the random terminal time is the first exit time of an SDE from a cylindrical domain. Thus, we give bounds for the discrete-time approximation error.. We conclude this part with numerical tests showing that this approach is effective.