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Dissertations / Theses on the topic 'Stochastic partial differential equations'
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1
Dareiotis, Anastasios Constantinos. "Stochastic partial differential and integro-differential equations." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/14186.
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In this work we present some new results concerning stochastic partial differential and integro-differential equations (SPDEs and SPIDEs) that appear in non-linear filtering. We prove existence and uniqueness of solutions of SPIDEs, we give a comparison principle and we suggest an approximation scheme for the non-local integral operators. Regarding SPDEs, we use techniques motivated by the work of De Giorgi, Nash, and Moser, in order to derive global and local supremum estimates, and a weak Harnack inequality.
2
Hofmanová, Martina. "Degenerate parabolic stochastic partial differential equations." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2013. http://tel.archives-ouvertes.fr/tel-00916580.
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In this thesis, we address several problems arising in the study of nondegenerate and degenerate parabolic SPDEs, stochastic hyperbolic conservation laws and SDEs with continues coefficients. In the first part, we are interested in degenerate parabolic SPDEs, adapt the notion of kinetic formulation and kinetic solution and establish existence, uniqueness as well as continuous dependence on initial data. As a preliminary result we obtain regularity of solutions in the nondegenerate case under the hypothesis that all the coefficients are sufficiently smooth and have bounded derivatives. In the second part, we consider hyperbolic conservation laws with stochastic forcing and study their approximations in the sense of Bhatnagar-Gross-Krook. In particular, we describe the conservation laws as a hydrodynamic limit of the stochastic BGK model as the microscopic scale vanishes. In the last part, we provide a new and fairly elementary proof of Skorkhod's classical theorem on existence of weak solutions to SDEs with continuous coefficients satisfying a suitable Lyapunov condition.
3
Matetski, Kanstantsin. "Discretisations of rough stochastic partial differential equations." Thesis, University of Warwick, 2016. http://wrap.warwick.ac.uk/81460/.
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This thesis consists of two parts, in both of which we consider approximations of rough stochastic PDEs and investigate convergence properties of the approximate solutions. In the first part we use the theory of (controlled) rough paths to define a solution for one-dimensional stochastic PDEs of Burgers type driven by an additive space-time white noise. We prove that natural numerical approximations of these equations converge to the solution of a corrected continuous equation and that their optimal convergence rate in the uniform topology (in probability) is arbitrarily close to 1/2 . In the second part of the thesis we develop a general framework for spatial discretisations of parabolic stochastic PDEs whose solutions are provided in the framework of the theory of regularity structures and which are functions in time. As an application, we show that the dynamical �43 model on the dyadic grid converges after renormalisation to its continuous counterpart. This result in particular implies that, as expected, the �43 measure is invariant for this equation and that the lifetime of its solutions is almost surely infinite for almost every initial condition.
4
Spantini, Alessio. "Preconditioning techniques for stochastic partial differential equations." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/82507.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2013.This thesis was scanned as part of an electronic thesis pilot project.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 149-155).
This thesis is about preconditioning techniques for time dependent stochastic Partial Differential Equations arising in the broader context of Uncertainty Quantification. State-of-the-art methods for an efficient integration of stochastic PDEs require the solution field to lie on a low dimensional linear manifold. In cases when there is not such an intrinsic low rank structure we must resort on expensive and time consuming simulations. We provide a preconditioning technique based on local time stretching capable to either push or keep the solution field on a low rank manifold with substantial reduction in the storage and the computational burden. As a by-product we end up addressing also classical issues related to long time integration of stochastic PDEs.
by Alessio Spantini.
S.M.
5
Prerapa, Surya Mohan. "Projection schemes for stochastic partial differential equations." Thesis, University of Southampton, 2009. https://eprints.soton.ac.uk/342800/.
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The focus of the present work is to develop stochastic reduced basis methods (SRBMs) for solving partial differential equations (PDEs) defined on random domains and nonlinear stochastic PDEs (SPDEs). SRBMs have been extended in the following directions: Firstly, an h-refinement strategy referred to as Multi-Element-SRBMs (ME-SRBMs) is developed for local refinement of the solution process. The random space is decomposed into subdomains where SRBMs are employed in each subdomain resulting in local response statistics. These local statistics are subsequently assimilated to compute the global statistics. Two types of preconditioning strategies namely global and local preconditioning strategies are discussed due to their merits such as degree of parallelizability and better convergence trends. The improved accuracy and convergence trends of ME-SRBMs are demonstrated by numerical investigation of stochastic steady state elasticity and stochastic heat transfer applications. The second extension involves the development of a computational approach employing SRBMs for solving linear elliptic PDEs defined on random domains. The key idea is to carry out spatial discretization of the governing equations using finite element (FE) methods and mesh deformation strategies. This results in a linear random algebraic system of equations whose coefficients of expansion can be computed nonintrusively either at the element or the global level. SRBMs are subsequently applied to the linear random algebraic system of equations to obtain the response statistics. We establish conditions that the input uncertainty model must satisfy to ensure the well-posedness of the problem. The proposed formulation is demonstrated on two and three dimensional model problems with uncertain boundaries undergoing steady state heat transfer. A large scale study involving a three-dimensional gas turbine model with uncertain boundary, has been presented in this context. Finally, a numerical scheme that combines SRBMs with the Picard iteration scheme is proposed for solving nonlinear SPDEs. The governing equations are linearized using the response process from the previous iteration and spatially discretized. The resulting linear random algebraic system of equations are solved to obtain the new response process which acts as a guess for the next iteration. These steps of linearization, spatial discretization, solving the system of equations and updating the current guess are repeated until the desired accuracy is achieved. The effectiveness and the limitations of the formulation are demonstrated employing numerical studies in nonlinear heat transfer and the one-dimensional Burger’s equation.
6
Zhang, Qi. "Stationary solutions of stochastic partial differential equations and infinite horizon backward doubly stochastic differential equations." Thesis, Loughborough University, 2008. https://dspace.lboro.ac.uk/2134/34040.
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In this thesis we study the existence of stationary solutions for stochastic partial differential equations. We establish a new connection between solutions of backward doubly stochastic differential equations (BDSDEs) on infinite horizon and the stationary solutions of the SPDEs. For this, we prove the existence and uniqueness of the L2ρ (Rd; R1) × L2ρ (Rd; Rd) valued solutions of BDSDEs with Lipschitz nonlinear term on both finite and infinite horizons, so obtain the solutions of initial value problems and the stationary weak solutions (independent of any initial value) of SPDEs. Also the L2ρ (Rd; R1) × L2ρ (Rd; Rd) valued BDSDE with non-Lipschitz term is considered. Moreover, we verify the time and space continuity of solutions of real-valued BDSDEs, so obtain the stationary stochastic viscosity solutions of real-valued SPDEs. The connection of the weak solutions of SPDEs and BDSDEs has independent interests in the areas of both SPDEs and BSDEs.
7
Mu, Tingshu. "Backward stochastic differential equations and applications : optimal switching, stochastic games, partial differential equations and mean-field." Thesis, Le Mans, 2020. http://www.theses.fr/2020LEMA1023.
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Cette thèse est relative aux Equations Différentielles Stochastique Rétrogrades (EDSRs) réfléchies avec deux obstacles et leurs applications aux jeux de switching de somme nulle, aux systèmes d’équations aux dérivées partielles, aux problèmes de mean-field. Il y a deux parties dans cette thèse. La première partie porte sur le switching optimal stochastique et est composée de deux travaux. Dans le premier travail, nous montrons l’existence de la solution d’un système d’EDSR réfléchies à obstacles bilatéraux interconnectés dans le cadre probabiliste général. Ce problème est lié à un jeu de switching de somme nulle. Ensuite nous abordons la question de l’unicité de la solution. Et enfin nous appliquons les résultats obtenus pour montrer que le système d’EDP associé à une unique solution au sens viscosité, sans la condition de monotonie habituelle. Dans le second travail, nous considérons aussi un système d’EDSRs réfléchies à obstacles bilatéraux interconnectés dans le cadre markovien. La différence avec le premier travail réside dans le fait que le switching ne s’opère pas de la même manière. Cette fois-ci quand le switching est opéré, le système est mis dans l’état suivant importe peu lequel des joueurs décide de switcher. Cette différence est fondamentale et complique singulièrement le problème de l’existence de la solution du système. Néanmoins, dans le cadre markovien nous montrons cette existence et donnons un résultat d’unicité en utilisant principalement la méthode de Perron. Ensuite, le lien avec un jeu de switching spécifique est établi dans deux cadres. Dans la seconde partie nous étudions les EDSR réfléchies unidimensionnelles à deux obstacles de type mean-field. Par la méthode du point fixe, nous montrons l’existence et l’unicité de la solution dans deux cadres, en fonction de l’intégrabilité des donnéesThis thesis is related to Doubly Reflected Backward Stochastic Differential Equations (DRBSDEs) with two obstacles and their applications in zero-sum stochastic switching games, systems of partial differential equations, mean-field problems.There are two parts in this thesis. The first part deals with optimal stochastic switching and is composed of two works. In the first work we prove the existence of the solution of a system of DRBSDEs with bilateral interconnected obstacles in a probabilistic framework. This problem is related to a zero-sum switching game. Then we tackle the problem of the uniqueness of the solution. Finally, we apply the obtained results and prove that, without the usual monotonicity condition, the associated PDE system has a unique solution in viscosity sense. In the second work, we also consider a system of DRBSDEs with bilateral interconnected obstacles in the markovian framework. The difference between this work and the first one lies in the fact that switching does not work in the same way. In this second framework, when switching is operated, the system is put in the following state regardless of which player decides to switch. This difference is fundamental and largely complicates the problem of the existence of the solution of the system. Nevertheless, in the Markovian framework we show this existence and give a uniqueness result by the Perron’s method. Later on, two particular switching games are analyzed.In the second part we study a one-dimensional Reflected BSDE with two obstacles of mean-field type. By the fixed point method, we show the existence and uniqueness of the solution in connection with the integrality of the data
8
Athreya, Siva. "Probability and semilinear partial differential equations /." Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/5799.
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Pätz, Torben [Verfasser]. "Segmentation of Stochastic Images using Stochastic Partial Differential Equations / Torben Pätz." Bremen : IRC-Library, Information Resource Center der Jacobs University Bremen, 2012. http://d-nb.info/1035219735/34.
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Pak, Alexey. "Stochastic partial differential equations with coefficients depending on VaR." Thesis, University of Warwick, 2017. http://wrap.warwick.ac.uk/93458/.
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In this paper we prove the well-posedness for a stochastic partial differential equation (SPDE) whose solution is a probability-measure-valued process. We allow the coefficients to depend on the median or, more generally, on the γ-quantile (or some its useful extensions) of the underlying distribution. Such SPDEs arise in many applications, for example, in auction system described in [2]. The well-posedness of this SPDE does not follow by standard arguments because the γ-quantile is not a continuous function on the space of probability measures equipped with the weak convergence.
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RINALDI, PAOLO. "A Novel Perturbative Approach to Stochastic Partial Differential Equations." Doctoral thesis, Università degli studi di Pavia, 2022. http://hdl.handle.net/11571/1447824.
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The topic of this thesis is the application of techniques proper of algebraic quantum field theory
(AQFT) to the analysis of stochastic partial differential equations (SPDEs), in particular to non-
linear ones. Despite being apparently so far apart, these two frameworks have a lot in common
and, probably, the most unexpected shared feature is the need of invoking renormalization.
Chapter 1 is devoted to recollecting some basic material about stochastic partial differential
equations, starting from some motivating examples, presenting a brief survey of the theory
of regularity structures and highlighting some notable technical results. In this chapter also
some further results of the author are discussed, in particular concerning a microlocal version
of the Young's product theorem and the formulation on smooth manifolds of the reconstruction
theorem in the framework of coherent germs of distributions.
The remaining Chapters are devoted to the main contribution of this Ph.D. thesis, namely
the microlocal approach to SPDEs. This provides a novel framework for the perturbative
analysis of a vast class of non-linear SPDEs. In particular, adapting techniques proper of
AQFT, such as microlocal analysis and the theory of the scaling degree, it allows to deal
with renormalization avoiding any regularization procedures and subtraction of in infinities. On
the contrary, it allows the explicit construction of finite renormalization constants and the
classification of the ambiguities arising as a consequence of the renormalization procedure.
The last chapter is devoted to the application of the general machinery discussed in the previous
two chapters to a specific example, namely the stochastic quantization equation. In this chapter we
make some explicit computations at first order in perturbation theory both for the expectation
value of the solution and for the two-point correlation function.
12
Philipowski, Robert. "Stochastic interacting particle systems and nonlinear partial differential equations from fluid mechanics." [S.l.] : [s.n.], 2007. http://deposit.ddb.de/cgi-bin/dokserv?idn=986005622.
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Ignatyev, Oleksiy. "The Compact Support Property for Hyperbolic SPDEs: Two Contrasting Equations." [Kent, Ohio] : Kent State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=kent1216323351.
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Thesis (Ph. D.)--Kent State University, 2008.Title from PDF t.p. (viewed Nov. 10, 2009). Advisor: Hassan Allouba. Keywords: stochastic partial differential equations; compact support property. Includes bibliographical references (p. 30).
14
McKay, Steven M. "Brownian Motion Applied to Partial Differential Equations." DigitalCommons@USU, 1985. https://digitalcommons.usu.edu/etd/6992.
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This work is a study of the relationship between Brownian motion and elementary, linear partial differential equations. In the text, I have shown that Brownian motion is a Markov process, and that Brownian motion itself, and certain Stochastic processes involving Brownian motion are also martingales. In particular, Dynkin's formula for Brownian motion was shown. Using Dynkin's formula and Brownian motion, I then constructed solutions for the classical Dirichlet problem and the heat equation, given by Δu=0 and ut= 1/2Δu+g, respectively. I have shown that the bounded solution is unique if Brownian motion will always exit the domain of the function once it has started at a point in the domain. The heat equation also has a unique bounded solution.
15
Emereuwa, Chigoziem A. "Homogenization of stochastic partial differential equations in perforated porous media." Thesis, University of Pretoria, 2019. http://hdl.handle.net/2263/77812.
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In this thesis, we study the homogenization of a stochastic model of groundwater
pollution in periodic porous media and the homogenization of a stochastic model
of a single-phase
uid
ow in partially ssured media.
In the rst study, we investigated the
ow of a
uid carrying reacting substances
through a porous medium. We modeled this
ow using a coupled system of equations;
the velocity of the
uid is modeled using steady Stokes equations, the concentration
of the solute while being moved by the
uid under the action of random
forces is modeled by a stochastic convection-di usion equation driven by a
Wiener type random force and the concentration of the solute on the surface of
the pore skeleton is modeled using reaction-di usion equations. The homogenization
process was carried out using the multiple scale expansion, Tartar's method
of oscillating test functions and stochastic calculus together with deep probability
compactness results due to Prokhorov and Skorokhod. This part of the thesis is
the rst in the scienti c literature dealing with the important problem of groundwater
pollution using stochastic partial di erential equations. Our results in this
regard are original. Also as a by-product of our work, we establish the rst homogenization
result for stochastic convection-di usion equation
The second study is devoted to a single-phase
ow under the in
uence of external
random forces through partially ssured media arising in reservoir engineering (oil
and gas industries). We undertake to model this
ow using a system of nonlinear
stochastic di usion equations with monotone operators in the pore system and the
ssure system; on the interface of the pores and ssures, we prescribe transmission
boundary conditions. We carried out the homogenization process using the
two-scale convergence method, Prokhorov- Skorokhod compactness process and
Minty's monotonicity method. While some works have been undertaken in the
deterministic case and in the case of nonlinear di usion equations with randomly
oscillating coe cients, our work is novel in the sense that it uses the more advanced
tool of stochastic partial di erential equations driven by random forces to
investigate the in
uence of random
uctuations on the
ow. To the best of our
knowledge, our work also initiates the study of stochastic evolution transmission
problems by means of homogenization.Thesis (PhD)--University of Pretoria, 2019.
Mathematics and Applied Mathematics
PhD
Unrestricted
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o, Perdomo Rafael Antonio. "Optimal control of stochastic partial differential equations in Banach spaces." Thesis, University of York, 2010. http://etheses.whiterose.ac.uk/1112/.
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In this thesis we study optimal control problems in Banach spaces for stochastic partial differential equations. We investigate two different approaches. In the first part we study Hamilton-Jacobi-Bellman equations (HJB) in Banach spaces associated with optimal feedback control of a class of non-autonomous semilinear stochastic evolution equations driven by additive noise. We prove the existence and uniqueness of mild solutions to HJB equations using the smoothing property of the transition evolution operator associated with the linearized stochastic equation. In the second part we study an optimal relaxed control problem for a class of autonomous semilinear stochastic stochastic PDEs on Banach spaces driven by multiplicative noise. The state equation is controlled through the nonlinear part of the drift coefficient and satisfies a dissipative-type condition with respect to the state variable. The main tools of our study are the factorization method for stochastic convolutions in UMD type-2 Banach spaces and certain compactness properties of the factorization operator and of the class of Young measures on Suslin metrisable control sets.
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Mai, Thanh Tan [Verfasser]. "Stochastic partial differential equations corresponding to time-inhomogeneous evolution equations / Thanh Tan Mai." München : Verlag Dr. Hut, 2012. http://d-nb.info/1029399719/34.
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von, Schwerin Erik. "Convergence rates of adaptive algorithms for stochastic and partial differential equations." Licentiate thesis, KTH, Numerical Analysis and Computer Science, NADA, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302.
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Schwerin, Erik von. "Convergence rates of adaptive algorithms for stochastic and partial differential equations /." Stockholm, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302.
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Leonhard, Claudine [Verfasser]. "Derivative-free numerical schemes for stochastic partial differential equations / Claudine Leonhard." Lübeck : Zentrale Hochschulbibliothek Lübeck, 2017. http://d-nb.info/1135168091/34.
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Neuß, Marius [Verfasser]. "Stochastic partial differential equations arising in self-organized criticality / Marius Neuß." Bielefeld : Universitätsbibliothek Bielefeld, 2021. http://d-nb.info/1231994762/34.
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Luo, Wuan Hou Thomas Y. "Wiener chaos expansion and numerical solutions of stochastic partial differential equations /." Diss., Pasadena, Calif. : Caltech, 2006. http://resolver.caltech.edu/CaltechETD:etd-05182006-173710.
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Cartwright, Madeleine Clare. "Collective coordinates approach for travelling waves in stochastic partial differential equations." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/25942.
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We propose a formal framework based on collective coordinates to reduce infinite-dimensional stochastic partial differential equations (SPDEs) with symmetry to a set of finite-dimensional stochastic differential equations which describe the shape of the solution and the dynamics along the symmetry group. We study dissipative and non-dissipative SPDEs that support travelling wave solutions. We find that the collective coordinate approach provides a remarkably good quantitative description of the shape and the position of the travelling wave. We corroborate our analytical results with numerical simulations of the full SPDEs.
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Yang, Juan. "Invariant measures for stochastic partial differential equations and splitting-up method for stochastic flows." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/invariant-measures-for-stochastic-partial-differential-equations-and-splittingup-method-for-stochastic-flows(36b3d40a-5094-4364-8732-12324ef3a72f).html.
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This thesis consists of two parts. We start with some background theory that will be used throughout the thesis. Then, in the first part, we investigate the existence and uniqueness of the solution of the stochastic partial differential equation with two reflecting walls. Then we establish the existence and uniqueness of invariant measure of this equation under some reasonable conditions. In the second part, we study the splitting-up method for approximating the solu- tions of stochastic Stokes equations using resolvent method.
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Wang, Xince. "Quasilinear PDEs and forward-backward stochastic differential equations." Thesis, Loughborough University, 2015. https://dspace.lboro.ac.uk/2134/17383.
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In this thesis, first we study the unique classical solution of quasi-linear second order parabolic partial differential equations (PDEs). For this, we study the existence and uniqueness of the $L^2_{\rho}( \mathbb{R}^{d}; \mathbb{R}^{d}) \otimes L^2_{\rho}( \mathbb{R}^{d}; \mathbb{R}^{k})\otimes L^2_{\rho}( \mathbb{R}^{d}; \mathbb{R}^{k\times d})$ valued solution of forward backward stochastic differential equations (FBSDEs) with finite horizon, the regularity property of the solution of FBSDEs and the connection between the solution of FBSDEs and the solution of quasi-linear parabolic PDEs. Then we establish their connection in the Sobolev weak sense, in order to give the weak solution of the quasi-linear parabolic PDEs. Finally, we study the unique weak solution of quasi-linear second order elliptic PDEs through the stationary solution of the FBSDEs with infinite horizon.
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Yeadon, Cyrus. "Approximating solutions of backward doubly stochastic differential equations with measurable coefficients using a time discretization scheme." Thesis, Loughborough University, 2015. https://dspace.lboro.ac.uk/2134/20643.
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It has been shown that backward doubly stochastic differential equations (BDSDEs) provide a probabilistic representation for a certain class of nonlinear parabolic stochastic partial differential equations (SPDEs). It has also been shown that the solution of a BDSDE with Lipschitz coefficients can be approximated by first discretizing time and then calculating a sequence of conditional expectations. Given fixed points in time and space, this approximation has been shown to converge in mean square. In this thesis, we investigate the approximation of solutions of BDSDEs with coefficients that are measurable in time and space using a time discretization scheme with a view towards applications to SPDEs. To achieve this, we require the underlying forward diffusion to have smooth coefficients and we consider convergence in a norm which includes a weighted spatial integral. This combination of smoother forward coefficients and weaker norm allows the use of an equivalence of norms result which is key to our approach. We additionally take a brief look at the approximation of solutions of a class of infinite horizon BDSDEs with a view towards approximating stationary solutions of SPDEs. Whilst we remain agnostic with regards to the implementation of our discretization scheme, our scheme should be amenable to a Monte Carlo simulation based approach. If this is the case, we propose that in addition to being attractive from a performance perspective in higher dimensions, such an approach has a potential advantage when considering measurable coefficients. Specifically, since we only discretize time and effectively rely on simulations of the underlying forward diffusion to explore space, we are potentially less vulnerable to systematically overestimating or underestimating the effects of coefficients with spatial discontinuities than alternative approaches such as finite difference or finite element schemes that do discretize space. Another advantage of the BDSDE approach is that it is possible to derive an upper bound on the error of our method for a fairly broad class of conditions in a single analysis. Furthermore, our conditions seem more general in some respects than is typically considered in the SPDE literature.
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Jin, Chao. "Parallel domain decomposition methods for stochastic partial differential equations and analysis of nonlinear integral equations." Connect to online resource, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3256468.
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von, Schwerin Erik. "Adaptivity for Stochastic and Partial Differential Equations with Applications to Phase Transformations." Doctoral thesis, KTH, Numerisk Analys och Datalogi, NADA, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4477.
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his work is concentrated on efforts to efficiently compute properties of systems, modelled by differential equations, involving multiple scales. Goal oriented adaptivity is the common approach to all the treated problems. Here the goal of a numerical computation is to approximate a functional of the solution to the differential equation and the numerical method is adapted to this task. The thesis consists of four papers. The first three papers concern the convergence of adaptive algorithms for numerical solution of differential equations; based on a posteriori expansions of global errors in the sought functional, the discretisations used in a numerical solution of the differential equiation are adaptively refined. The fourth paper uses expansion of the adaptive modelling error to compute a stochastic differential equation for a phase-field by coarse-graining molecular dynamics. An adaptive algorithm aims to minimise the number of degrees of freedom to make the error in the functional less than a given tolerance. The number of degrees of freedom provides the convergence rate of the adaptive algorithm as the tolerance tends to zero. Provided that the computational work is proportional to the degrees of freedom this gives an estimate of the efficiency of the algorithm. The first paper treats approximation of functionals of solutions to second order elliptic partial differential equations in bounded domains of ℝd, using isoparametric $d$-linear quadrilateral finite elements. For an adaptive algorithm, an error expansion with computable leading order term is derived %. and used in a computable error density, which is proved to converge uniformly as the mesh size tends to zero. For each element an error indicator is defined by the computed error density multiplying the local mesh size to the power of 2+d. The adaptive algorithm is based on successive subdivisions of elements, where it uses the error indicators. It is proved, using the uniform convergence of the error density, that the algorithm either reduces the maximal error indicator with a factor or stops; if it stops, then the error is asymptotically bounded by the tolerance using the optimal number of elements for an adaptive isotropic mesh, up to a problem independent factor. Here the optimal number of elements is proportional to the d/2 power of the Ldd+2 quasi-norm of the error density, whereas a uniform mesh requires a number of elements proportional to the d/2 power of the larger L1 norm of the same error density to obtain the same accuracy. For problems with multiple scales, in particular, these convergence rates may differ much, even though the convergence order may be the same. The second paper presents an adaptive algorithm for Monte Carlo Euler approximation of the expected value E[g(X(τ),\τ)] of a given function g depending on the solution X of an \Ito\ stochastic differential equation and on the first exit time τ from a given domain. An error expansion with computable leading order term for the approximation of E[g(X(T))] with a fixed final time T>0 was given in~[Szepessy, Tempone, and Zouraris, Comm. Pure and Appl. Math., 54, 1169-1214, 2001]. This error expansion is now extended to the case with stopped diffusion. In the extension conditional probabilities are used to estimate the first exit time error, and difference quotients are used to approximate the initial data of the dual solutions. For the stopped diffusion problem the time discretisation error is of order N-1/2 for a method with N uniform time steps. Numerical results show that the adaptive algorithm improves the time discretisation error to the order N-1, with N adaptive time steps. The third paper gives an overview of the application of the adaptive algorithm in the first two papers to ordinary, stochastic, and partial differential equation. The fourth paper investigates the possibility of computing some of the model functions in an Allen--Cahn type phase-field equation from a microscale model, where the material is described by stochastic, Smoluchowski, molecular dynamics. A local average of contributions to the potential energy in the micro model is used to determine the local phase, and a stochastic phase-field model is computed by coarse-graining the molecular dynamics. Molecular dynamics simulations on a two phase system at the melting point are used to compute a double-well reaction term in the Allen--Cahn equation and a diffusion matrix describing the noise in the coarse-grained phase-field.QC 20100823
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Schwerin, Erik von. "Adaptivity for stochastic and partial differential equations with applications to phase transformations /." Stockholm : Numerisk analys och datalogi, Kungliga Tekniska högskolan, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4477.
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Wieland, Bernhard [Verfasser]. "Reduced basis methods for partial differential equations with stochastic influences / Bernhard Wieland." Ulm : Universität Ulm. Fakultät für Mathematik und Wirtschaftswissenschaften, 2013. http://d-nb.info/1038004780/34.
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Sturm, Anja Karin. "On spatially structured population processes and relations to stochastic partial differential equations." Thesis, University of Oxford, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.249618.
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Stanciulescu, Vasile Nicolae. "Selected topics in Dirichlet problems for linear parabolic stochastic partial differential equations." Thesis, University of Leicester, 2010. http://hdl.handle.net/2381/8271.
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This thesis is devoted to the study of Dirichlet problems for some linear parabolic SPDEs. Our aim in it is twofold. First, we consider SPDEs with deterministic coefficients which are smooth up to some order of regularity. We establish some theoretical results in terms of existence, uniqueness and regularity of the classical solution to the considered problem. Then, we provide the probabilistic representations (the averaging-over-characteristic formulas of its solution. We, thereafter, construct numerical methods for it. The methods are based on the averaging-over-characteristic formula and the weak-sense numerical integration of ordinary stochastic differential equations in bounded domains. Their orders of convergence in the mean-square sense and in the sense of almost sure convergence are obtained. The Monte Carlo technique is used for practical realization of the methods. Results of some numerical experiments are presented. These results are in agreement with the theoretical findings. Second, we construct the solution of a class of one dimensional stochastic linear heat equations with drift in the first Wiener chaos, deterministic initial condition and which are driven by a space-time white noise and the white noise. This is done by giving explicitly its Wiener chaos decomposition. We also prove its uniqueness in the weak sense. Then we use the chaos expansion in order to show that the unique weak solution is an analytic functional with finite moments of all orders. The chaos decomposition is also utilized as a very useful tool for obtaining a continuity property of the solution.
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Yang, Weiye. "Stochastic analysis and stochastic PDEs on fractals." Thesis, University of Oxford, 2018. http://ora.ox.ac.uk/objects/uuid:43a7af74-c531-424a-9f3d-4277138affbb.
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Stochastic analysis on fractals is, as one might expect, a subfield of analysis on fractals. An intuitive starting point is to observe that on many fractals, one can define diffusion processes whose law is in some sense invariant with respect to the symmetries and self-similarities of the fractal. These can be interpreted as fractal-valued counterparts of standard Brownian motion on Rd. One can study these diffusions directly, for example by computing heat kernel and hitting time estimates. On the other hand, by associating the infinitesimal generator of the fractal-valued diffusion with the Laplacian on Rd, it is possible to pose stochastic partial differential equations on the fractal such as the stochastic heat equation and stochastic wave equation. In this thesis we investigate a variety of questions concerning the properties of diffusions on fractals and the parabolic and hyperbolic SPDEs associated with them. Key results include an extension of Kolmogorov's continuity theorem to stochastic processes indexed by fractals, and existence and uniqueness of solutions to parabolic SPDEs on fractals with Lipschitz data.
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Leahy, James-Michael. "On parabolic stochastic integro-differential equations : existence, regularity and numerics." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/10569.
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In this thesis, we study the existence, uniqueness, and regularity of systems of degenerate linear stochastic integro-differential equations (SIDEs) of parabolic type with adapted coefficients in the whole space. We also investigate explicit and implicit finite difference schemes for SIDEs with non-degenerate diffusion. The class of equations we consider arise in non-linear filtering of semimartingales with jumps. In Chapter 2, we derive moment estimates and a strong limit theorem for space inverses of stochastic flows generated by Lévy driven stochastic differential equations (SDEs) with adapted coefficients in weighted Hölder norms using the Sobolev embedding theorem and the change of variable formula. As an application of some basic properties of flows of Weiner driven SDEs, we prove the existence and uniqueness of classical solutions of linear parabolic second order stochastic partial differential equations (SPDEs) by partitioning the time interval and passing to the limit. The methods we use allow us to improve on previously known results in the continuous case and to derive new ones in the jump case. Chapter 3 is dedicated to the proof of existence and uniqueness of classical solutions of degenerate SIDEs using the method of stochastic characteristics. More precisely, we use Feynman-Kac transformations, conditioning, and the interlacing of space inverses of stochastic flows generated by SDEs with jumps to construct solutions. In Chapter 4, we prove the existence and uniqueness of solutions of degenerate linear stochastic evolution equations driven by jump processes in a Hilbert scale using the variational framework of stochastic evolution equations and the method of vanishing viscosity. As an application, we establish the existence and uniqueness of solutions of degenerate linear stochastic integro-differential equations in the L2-Sobolev scale. Finite difference schemes for non-degenerate SIDEs are considered in Chapter 5. Specifically, we study the rate of convergence of an explicit and an implicit-explicit finite difference scheme for linear SIDEs and show that the rate is of order one in space and order one-half in time.
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Ali, Zakaria Idriss. "Existence result for a class of stochastic quasilinear partial differential equations with non-standard growth." Diss., University of Pretoria, 2010. http://hdl.handle.net/2263/29519.
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In this dissertation, we investigate a very interesting class of quasi-linear stochastic partial differential equations. The main purpose of this article is to prove an existence result for such type of stochastic differential equations with non-standard growth conditions. The main difficulty in the present problem is that the existence cannot be easily retrieved from the well known results under Lipschitz type of growth conditions [42].Dissertation (MSc)--University of Pretoria, 2010.
Mathematics and Applied Mathematics
unrestricted
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Deb, Manas Kumar. "Solution of stochastic partial differential equations (SPDEs) using Galerkin method : theory and applications /." Digital version accessible at:, 2000. http://wwwlib.umi.com/cr/utexas/main.
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Soomro, Inayatullah. "Mathematical and computational modelling of stochastic partial differential equations applied to advanced methods." Thesis, University of Central Lancashire, 2016. http://clok.uclan.ac.uk/20422/.
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Mathematical modelling and simulations were carried to study diblock copolymer system confined in circular annular pores, cylindrical pores and spherical pores using Cell Dynamics simulation (CDS) method employed in physically motivated discretization. The lamella, cylindrical and spherical forming systems were studied in the neutral surfaces and the wetting surfaces. To employ CDS method in polar, cylindrical and spherical coordinates, the Laplacian operators were discretized and isotropised in polar, cylindrical and spherical coordinate systems.
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Hall, Eric Joseph. "Accelerated numerical schemes for deterministic and stochastic partial differential equations of parabolic type." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/8038.
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First we consider implicit finite difference schemes on uniform grids in time and space for second order linear stochastic partial differential equations of parabolic type. Under sufficient regularity conditions, we prove the existence of an appropriate asymptotic expansion in powers of the the spatial mesh and hence we apply Richardson's method to accelerate the convergence with respect to the spatial approximation to an arbitrarily high order. Then we extend these results to equations where the parabolicity condition is allowed to degenerate. Finally, we consider implicit finite difference approximations for deterministic linear second order partial differential equations of parabolic type and give sufficient conditions under which the approximations in space and time can be simultaneously accelerated to an arbitrarily high order.
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Xu, Tiange. "Large deviations and invariant measures for stochastic partial differential equations in infinite dimensions." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.496642.
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This thesis consists of two parts. We start with some background theory that will be used throughout the thesis. Then, in the first part, we investigate the invariant measures of stochastic evolution equations of pure jump type and obtain a characterization of invariant measures. As an application, it is shown that the equation has a unique invariant probability measure under some reasonable conditions.
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SILVA, CLAUSON CARVALHO DA. "STOCHASTIC REPRESENTATION FOR SOLUTIONS OF THE DIRICHLET PROBLEM FOR ELLIPTIC PARTIAL DIFFERENTIAL EQUATIONS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2016. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=27261@1.
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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIROCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
FUNDAÇÃO DE APOIO À PESQUISA DO ESTADO DO RIO DE JANEIRO
PROGRAMA DE SUPORTE À PÓS-GRADUAÇÃO DE INSTS. DE ENSINO
BOLSA NOTA 10
Como motivação, apresentaremos alguns problemas que ilustram a conexão entre a teoria da probabilidade e algumas equações diferenciais parciais. Suas soluções mesclam os dois assuntos e provocam a suspeita de que alguns processos estocásticos e operadores diferenciais caminham juntos. Em seguida, exibiremos a teoria das difusões de Itô. Mostraremos algumas de suas características, como a propriedade de Markov e cada um destes processos possuirá o que chamaremos de gerador infinitesimal da difusão. Este será um operador diferencial de segunda ordem cujo estudo detalhado revela características do processo. Apresentaremos também a fórmula de Dynkin. Com essas ferramentas probabilísticas, encontraremos uma representação estocástica para a solução do problema de Dirichlet para operadores diferenciais elípticos, generalizando as soluções dos problemas inicialmente propostos.
Firstly, for motivation purposes, we briefly present a few problems mixing notions of probability theory and of partial differential equations (PDE). In discussing the solution to such problems it will become apparent that some stochastic process and differential equations walk together. Next, we introduce a class of stochastic processes called the Ito diffusions, and some of its features such as the Markov property. Each such process has an associated linear operator the, so called, infinitesimal generator. This operator acts as a second-order differential operator on smooth functions, and controls the LOCAL behavior of these diffusions. We discuss these features together with Dynkin s formula a convenient relation derived from the infinitesimal generator, which informs us about the AVERAGE behavior of the diffusion. Finally, we apply these probabilistic tools to find a formula for the solution of the Dirichlet problem for a somewhat general linear elliptic second order PDE. This formula connects the solution of the PDE to the aggregated/average behavior and associated (Ito) diffusion. This type of stochastic representation generalizes the solution method of the problems firstly discussed.
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Bujok, Karolina Edyta. "Numerical solutions to a class of stochastic partial differential equations arising in finance." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:d2e76713-607b-4f26-977a-ac4df56d54f2.
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We propose two alternative approaches to evaluate numerically credit basket derivatives in a N-name structural model where the number of entities, N, is large, and where the names are independent and identically distributed random variables conditional on common random factors. In the first framework, we treat a N-name model as a set of N Bernoulli random variables indicating a default or a survival. We show that certain expected functionals of the proportion LN of variables in a given state converge at rate 1/N as N [right arrow - infinity]. Based on these results, we propose a multi-level simulation algorithm using a family of sequences with increasing length, to obtain estimators for these expected functionals with a mean-square error of epsilon 2 and computational complexity of order epsilon−2, independent of N. In particular, this optimal complexity order also holds for the infinite-dimensional limit. Numerical examples are presented for tranche spreads of basket credit derivatives. In the second framework, we extend the approximation of Bush et al. [13] to a structural jump-diffusion model with discretely monitored defaults. Under this approach, a N-name model is represented as a system of particles with an absorbing boundary that is active in a discrete time set, and the loss of a portfolio is given as the function of empirical measure of the system. We show that, for the infinite system, the empirical measure has a density with respect to the Lebesgue measure that satisfies a stochastic partial differential equation. Then, we develop an algorithm to efficiently estimate CDO index and tranche spreads consistent with underlying credit default swaps, using a finite difference simulation for the resulting SPDE. We verify the validity of this approximation numerically by comparison with results obtained by direct Monte Carlo simulation of the basket constituents. A calibration exercise assesses the flexibility of the model and its extensions to match CDO spreads from precrisis and crisis periods.
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Huré, Come. "Numerical methods and deep learning for stochastic control problems and partial differential equations." Thesis, Sorbonne Paris Cité, 2019. http://www.theses.fr/2019USPCC052.
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La thèse porte sur les schémas numériques pour les problèmes de décisions Markoviennes (MDPs), les équations aux dérivées partielles (EDPs), les équations différentielles stochastiques rétrogrades (ED- SRs), ainsi que les équations différentielles stochastiques rétrogrades réfléchies (EDSRs réfléchies). La thèse se divise en trois parties.La première partie porte sur des méthodes numériques pour résoudre les MDPs, à base de quan- tification et de régression locale ou globale. Un problème de market-making est proposé: il est résolu théoriquement en le réécrivant comme un MDP; et numériquement en utilisant le nouvel algorithme. Dans un second temps, une méthode de Markovian embedding est proposée pour réduire des prob- lèmes de type McKean-Vlasov avec information partielle à des MDPs. Cette méthode est mise en œuvre sur trois différents problèmes de type McKean-Vlasov avec information partielle, qui sont par la suite numériquement résolus en utilisant des méthodes numériques à base de régression et de quantification.Dans la seconde partie, on propose de nouveaux algorithmes pour résoudre les MDPs en grande dimension. Ces derniers reposent sur les réseaux de neurones, qui ont prouvé en pratique être les meilleurs pour apprendre des fonctions en grande dimension. La consistance des algorithmes proposés est prouvée, et ces derniers sont testés sur de nombreux problèmes de contrôle stochastique, ce qui permet d’illustrer leurs performances.Dans la troisième partie, on s’intéresse à des méthodes basées sur les réseaux de neurones pour résoudre les EDPs, EDSRs et EDSRs réfléchies. La convergence des algorithmes proposés est prouvée; et ces derniers sont comparés à d’autres algorithmes récents de la littérature sur quelques exemples, ce qui permet d’illustrer leurs très bonnes performancesThe present thesis deals with numerical schemes to solve Markov Decision Problems (MDPs), partial differential equations (PDEs), quasi-variational inequalities (QVIs), backward stochastic differential equations (BSDEs) and reflected backward stochastic differential equations (RBSDEs). The thesis is divided into three parts.The first part focuses on methods based on quantization, local regression and global regression to solve MDPs. Firstly, we present a new algorithm, named Qknn, and study its consistency. A time-continuous control problem of market-making is then presented, which is theoretically solved by reducing the problem to a MDP, and whose optimal control is accurately approximated by Qknn. Then, a method based on Markovian embedding is presented to reduce McKean-Vlasov control prob- lem with partial information to standard MDP. This method is applied to three different McKean- Vlasov control problems with partial information. The method and high accuracy of Qknn is validated by comparing the performance of the latter with some finite difference-based algorithms and some global regression-based algorithm such as regress-now and regress-later.In the second part of the thesis, we propose new algorithms to solve MDPs in high-dimension. Neural networks, combined with gradient-descent methods, have been empirically proved to be the best at learning complex functions in high-dimension, thus, leading us to base our new algorithms on them. We derived the theoretical rates of convergence of the proposed new algorithms, and tested them on several relevant applications.In the third part of the thesis, we propose a numerical scheme for PDEs, QVIs, BSDEs, and RBSDEs. We analyze the performance of our new algorithms, and compare them to other ones available in the literature (including the recent one proposed in [EHJ17]) on several tests, which illustrates the efficiency of our methods to estimate complex solutions in high-dimension.Keywords: Deep learning, neural networks, Stochastic control, Markov Decision Process, non- linear PDEs, QVIs, optimal stopping problem BSDEs, RBSDEs, McKean-Vlasov control, perfor- mance iteration, value iteration, hybrid iteration, global regression, local regression, regress-later, quantization, limit order book, pure-jump controlled process, algorithmic-trading, market-making, high-dimension
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Seadler, Bradley T. "Signed-Measure Valued Stochastic Partial Differential Equations with Applications in 2D Fluid Dynamics." Case Western Reserve University School of Graduate Studies / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1333062148.
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Manna, Utpal. "Harmonic and stochastic analysis aspects of the fluid dynamics equations." Laramie, Wyo. : University of Wyoming, 2007. http://proquest.umi.com/pqdweb?did=1414120661&sid=1&Fmt=2&clientId=18949&RQT=309&VName=PQD.
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Lee, Jangwoon. "Analysis and finite element approximations of stochastic optimal control problems constrained by stochastic elliptic partial differential equations." [Ames, Iowa : Iowa State University], 2008.
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Swanson, Jason. "Variations of stochastic processes : alternative approaches /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/5733.
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Blöthner, Florian [Verfasser]. "Non-Uniform Semi-Discretization of Linear Stochastic Partial Differential Equations in R / Florian Blöthner." München : Verlag Dr. Hut, 2019. http://d-nb.info/1181514207/34.
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LOBAO, WALDIR JESUS DE ARAUJO. "SOLUTION OF ORDINARY, PARTIAL AND STOCHASTIC DIFFERENTIAL EQUATIONS BY GENETIC PROGRAMMING AND AUTOMATIC DIFFERENTIATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2015. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=29824@1.
Full textAbstract:
PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIROCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
O presente trabalho teve como objetivo principal investigar o potencial de algoritmos computacionais evolutivos, construídos a partir das técnicas de programação genética, combinados com diferenciação automática, na obtenção de soluções analíticas, exatas ou aproximadas, para problemas de equações diferenciais ordinárias (EDO), parciais (EDP) e estocásticas. Com esse intuito, e utilizando-se o ambiente de programação Matlab, diversos algoritmos foram elaborados e soluções analíticas de diferentes tipos de equações diferenciais foram determinadas. No caso das equações determinísticas, EDOs e EDPs, foram abordados problemas de diferentes graus de dificuldade, do básico até problemas complexos como o da equação do calor e a equação de Schrödinger para o átomo de hélio. Os resultados obtidos são promissores, com soluções exatas para a grande maioria dos problemas tratados e que atestam, empiricamente, a consistência e robustez da metodologia proposta. Com relação às equações estocásticas, o trabalho apresenta uma nova proposta de solução e metodologia alternativa para a precificação de opções europeias, de compra e de venda, e realiza algumas aplicações para o mercado brasileiro, com ações da Petrobras e da Vale. Além destas aplicações, são apresentadas as soluções de alguns modelos clássicos, usualmente utilizados na modelagem de preços e retornos de ativos financeiros, como, por exemplo, o movimento Browniano geométrico. De uma forma geral, os resultados obtidos nas aplicações indicam que a metodologia proposta nesta tese pode ser uma alternativa eficiente na modelagem de problemas científicos complexos.
The main objective of this work was to investigate the potential of evolutionary algorithms, built from genetic programming techniques and combined with automatic differentiation, in obtaining exact or approximate analytical solutions for problems of ordinary (ODE), partial (PDE), and stochastic differential equations. To this end, and using the Matlab programming environment, several algorithms were developed and analytical solutions of different types of differential equations were determined. In the case of deterministic equations, ODE and PDE problems of varying degrees of difficulty were discussed, from basic to complex problems such as the heat equation and the Schrödinger equation for the helium atom. The results are promising, including exact solutions for the vast majority of the problems treated, which attest empirically the consistency and robustness of the proposed methodology. Regarding the stochastic equations, the work presents a new proposal for a solution and alternative methodology for European options pricing, buying and selling, and performs some applications for the Brazilian market, with stock prices of Petrobras and Vale. In addition to these applications, there are presented solutions of some classical models, usually used in the modeling of prices and returns of financial assets, such as the geometric Brownian motion. In a general way, the results obtained in applications indicate that the methodology proposed in this dissertation can be an efficient alternative in modeling complex scientific problems.
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Berger, David [Verfasser]. "Infinitely divisible and related distributions and Lévy driven stochastic partial differential equations / David Berger." Ulm : Universität Ulm, 2020. http://d-nb.info/1205001735/34.
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Mohammed, Wael Wagih Elbayoumi [Verfasser], and Dirk [Akademischer Betreuer] Blömker. "Multiscale Analysis of Stochastic Partial Differential Equations / Wael Wagih Elbayoumi Mohammed. Betreuer: Dirk Blömker." Augsburg : Universität Augsburg, 2012. http://d-nb.info/1077700873/34.
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