Bibliografías temáticas / Stochastic analysis

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Literatura académica sobre el tema "Stochastic analysis"

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Publicado: 4 de junio de 2021
Última modificación: 9 de junio de 2025

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Artículos de revistas sobre el tema "Stochastic analysis"

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PE and P. Malliavin. "Stochastic Analysis." Journal of the American Statistical Association 93, no. 441 (March 1998): 411. http://dx.doi.org/10.2307/2669659.

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Hu, Peng, та Chengming Huang. "The StochasticΘ-Method for Nonlinear Stochastic Volterra Integro-Differential Equations". Abstract and Applied Analysis 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/583930.

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The stochasticΘ-method is extended to solve nonlinear stochastic Volterra integro-differential equations. The mean-square convergence and asymptotic stability of the method are studied. First, we prove that the stochasticΘ-method is convergent of order1/2in mean-square sense for such equations. Then, a sufficient condition for mean-square exponential stability of the true solution is given. Under this condition, it is shown that the stochasticΘ-method is mean-square asymptotically stable for every stepsize if1/2≤θ≤1and when0≤θ<1/2, the stochasticΘ-method is mean-square asymptotically stable for some small stepsizes. Finally, we validate our conclusions by numerical experiments.
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Markus, L., and A. Weerasinghe. "Stochastic oscillators." Journal of Differential Equations 71, no. 2 (February 1988): 288–314. http://dx.doi.org/10.1016/0022-0396(88)90029-0.

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Sankar, T. S., S. A. Ramu, and R. Ganesan. "Stochastic Finite Element Analysis for High Speed Rotors." Journal of Vibration and Acoustics 115, no. 1 (January 1, 1993): 59–64. http://dx.doi.org/10.1115/1.2930315.

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The general problem of the dynamic response of highspeed rotors is considered in which certain system parameters may have a spatial stochastic variation. In particular the elastic modulus and mass density of a rotating shaft are described through one dimensional stochastic field functions so that the imperfections in manufacture and measurement can be accounted for. The stochastic finite element method is developed so that the variability of the response of the rotor can be interpreted in terms of the variation of the material property. As an illustration the whirl speed analysis is performed to determine the stochastics of whirl speeds and modes through the solution of a random eigenvalue problem associated with a non self-adjoint system. Numerical results are also presented.
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Hengki, Tamando Sihotang, Efendi Syahril, Zarlis Muhammad, and Mawengkang Herman. "Data driven approach for stochastic data envelopment analysis." Bulletin of Electrical Engineering and Informatics 11, no. 3 (June 1, 2022): 1497~1504. https://doi.org/10.11591/eei.v11i3.3660.

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Decision making based on data driven deals with a large amount of data will evaluate the process's effectiveness. Evaluate effectiveness in this paper is measure of performance efficiency of data envelopment analysis (DEA) method in this study is the approach with uncertainty problems. This study proposed a new method called the robust stochastic DEA (RSDEA) to approach performance efficiency in tackling uncertainty problems (i.e., stochastic and robust optimization). The RSDEA method develops to combine the stochastics DEA (SDEA) formulation method and Robust Optimization. The numerical example demonstrates the performance efficiency of the proposed formulation method, with the results performing confirmed that the efficiency value is 89%.
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Zhao, Wenqiang, and Yangrong Li. "Existence of Random Attractors for ap-Laplacian-Type Equation with Additive Noise." Abstract and Applied Analysis 2011 (2011): 1–21. http://dx.doi.org/10.1155/2011/616451.

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We first establish the existence and uniqueness of a solution for a stochasticp-Laplacian-type equation with additive white noise and show that the unique solution generates a stochastic dynamical system. By using the Dirichlet forms of Laplacian and an approximation procedure, the nonlinear obstacle, arising from the additive noise is overcome when we make energy estimate. Then, we obtain a random attractor for this stochastic dynamical system. Finally, under a restrictive assumption on the monotonicity coefficient, we find that the random attractor consists of a single point, and therefore the system possesses a unique stationary solution.
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IMKELLER, PETER, and ADAM HUGH MONAHAN. "CONCEPTUAL STOCHASTIC CLIMATE MODELS." Stochastics and Dynamics 02, no. 03 (September 2002): 311–26. http://dx.doi.org/10.1142/s0219493702000443.

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From July 9 to 11, 2001, 50 researchers from the fields of climate dynamics and stochastic analysis met in Chorin, Germany, to discuss the idea of stochastic models of climate. The present issue of Stochastics and Dynamics collects several papers from this meeting. In this introduction to the volume, the idea of simple conceptual stochastic climate models is introduced amd recent results in the mathematically rigorous development and analysis of such models are reviewed. As well, a brief overview of the application of ideas from stochastic dynamics to simple models of the climate system is given.
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Ocone, Daniel. "Stochastic calculus of variations for stochastic partial differential equations." Journal of Functional Analysis 79, no. 2 (August 1988): 288–331. http://dx.doi.org/10.1016/0022-1236(88)90015-8.

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Sihotang, Hengki Tamando, Syahril Efendi, Muhammad Zarlis, and Herman Mawengkang. "Data driven approach for stochastic data envelopment analysis." Bulletin of Electrical Engineering and Informatics 11, no. 3 (June 1, 2022): 1497–504. http://dx.doi.org/10.11591/eei.v11i3.3660.

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Decision making based on data driven deals with a large amount of data will evaluate the process's effectiveness. Evaluate effectiveness in this paper is measure of performance efficiency of data envelopment analysis (DEA) method in this study is the approach with uncertainty problems. This study proposed a new method called the robust stochastic DEA (RSDEA) to approach performance efficiency in tackling uncertainty problems (i.e., stochastic and robust optimization). The RSDEA method develops to combine the stochastics DEA (SDEA) formulation method and Robust Optimization. The numerical example demonstrates the performance efficiency of the proposed formulation method, with the results performing confirmed that the efficiency value is 89%.
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OGURA, Yukio. "Stochastic Fuzzy Analysis." Journal of Japan Society for Fuzzy Theory and Systems 10, no. 6 (1998): 1012–19. http://dx.doi.org/10.3156/jfuzzy.10.6_1012.

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Tesis sobre el tema "Stochastic analysis"

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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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Ozkan, Pelin. "Analysis Of Stochastic And Non-stochastic Volatility Models." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/3/12605421/index.pdf.

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Changing in variance or volatility with time can be modeled as deterministic by using autoregressive conditional heteroscedastic (ARCH) type models, or as stochastic by using stochastic volatility (SV) models. This study compares these two kinds of models which are estimated on Turkish / USA exchange rate data. First, a GARCH(1,1) model is fitted to the data by using the package E-views and then a Bayesian estimation procedure is used for estimating an appropriate SV model with the help of Ox code. In order to compare these models, the LR test statistic calculated for non-nested hypotheses is obtained.
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Binotto, Giulia. "Contributions to stochastic analysis." Doctoral thesis, Universitat de Barcelona, 2018. http://hdl.handle.net/10803/565571.

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The aim of this dissertation is to present some new results on stochastic analysis. It consists on three works that deal with two Gaussian processes: the Brownian motion and the fractional Brownian motion with Hurst parameter H less than 1/2. In the first work we construct a family of processes, from a single Poisson process and a sequence of independent random variables with common Bernoulli distribution, that converges in law to a complex Brownian motion. We find realizations of these processes that converge almost surely to the complex Brownian motion, uniformly on the unit time interval, and we derive the rate of convergence. In the second work, we establish the weak convergence, in the topology of the Skorohod space, of the symmetric Riemann sums for functionals of the fractional Brownian motion when the Hurst parameter takes a critical value that depends on the chosen measure. As a consequence, we derive a change-of-variable formula in distribution, where the correction term is a stochastic integral with respect to a Brownian motion that is independent of the fractional Brownian motion. The last work is devoted to prove that, when the delay goes to zero, the solution of delay differential equations driven by a Hölder continuous function of order in (1/3,1/2) converges with the supremum norm to the solution of the equation without delay.<br>L’objectiu d’aquesta tesi és presentar alguns resultats innovadors en el camp de l’anàlisi estocàstica. Proposem tres treballs que tracten amb dos processos Gaussians: el moviment Brownià i el moviment Brownià fraccionari amb paràmetre de Hurst menor que 1/2. En el primer treball, construïm una família de processos, a partir d’un procés de Poisson i d’una seqüència de variables aleatòries independents amb distribució de Bernoulli, que convergeix en llei cap a un moviment Brownià complex. Trobem realitzacions d’aquests processos que convergeixen quasi segurament a un moviment Brownià complex, uniformement a l’interval de temps unitat. En derivem també la velocitat de convergència. En el segon treball, determinem la convergència feble, en la topologia de l’espai de Skorohod, de les sumes de Riemann simètriques per funcionals del moviment Brownià fraccionari quan el paràmetre de Hurst pren un valor crític que depèn de la mesura considerada. Com a conseqüència, derivem una fórmula de canvi de variable en distribució, on el terme de correcció és una integral estocàstica amb respecte a un moviment Brownià independent del moviment Brownià fraccionari. En l’últim treball demostrem que, quan el retard tendeix a zero, la solució d’equacions diferencials amb retard dirigides per una funció Hölder contínua amb ordre a (1/3,1/2) convergeix en la norma del suprem a la solució d’equacions sense retard.
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Davies, M. J. "Topics in stochastic analysis." Thesis, Swansea University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.636421.

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This thesis uses Nelson's stochastic mechanics to study a variety of problems. These include point sources, particles in a constant magnetic field with oscillator potentials and Brownian Motion with a constant drift. By using a finite difference approximation Chapter 1 gives an account of the Stochastic Variational Principle for stochastic mechanics based on Carlen's approach. It is shown that every diffusion satisfying the dynamical law of stochastic mechanics corresponds to a solution of the Schröinger equation. Chapter 2 is concerned with point sources and gives a brief account of Nelson's work in this field as well as examples of monochromatic one and two point particle sources as elucidated by Truman et al. The generator of the radial motion for a particle emitted by a point source is shown to be the generator of Brownian Motion with a constant drift. The transition density and expected first hitting times for this process are then derived explicitly. Chapter 3 gives a resuméof Shucker's result for sample paths of the Nelson stochastic process governed by the free wave function. Analogues of Shucker's result for the initial Gaussian wave function in the presence of a constant magnetic field together with positive or negative harmonic oscillator potentials are then proved. Finally Chapter 4 deals with the case of one dimensional Brownian Motion with a constant drift <i>k</i> on the half line (0,∞) with 0 accessible and ∞ inaccessible. Following some earlier work of Mandl the transition density for such a process is obtained explicitly for the most general boundary conditions leading to continuous sample paths and a contraction semigroup on <i>C</i>(0,∞) with generatorhskip 1.5cm. Usingvarious expectation values we obtain the distribution of first hitting times and last exit times for these processes.
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Nadakuditi, Rajesh Rao. "Applied stochastic Eigen-analysis." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/38538.

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Thesis (Ph. D.)--Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; and the Woods Hole Oceanographic Institution), 2006.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Also issued in pages. Barker Engineering Library copy: issued in pages.<br>Includes bibliographical references (leaves 193-[201]).<br>The first part of the dissertation investigates the application of the theory of large random matrices to high-dimensional inference problems when the samples are drawn from a multivariate normal distribution. A longstanding problem in sensor array processing is addressed by designing an estimator for the number of signals in white noise that dramatically outperforms that proposed by Wax and Kailath. This methodology is extended to develop new parametric techniques for testing and estimation. Unlike techniques found in the literature, these exhibit robustness to high-dimensionality, sample size constraints and eigenvector misspecification. By interpreting the eigenvalues of the sample covariance matrix as an interacting particle system, the existence of a phase transition phenomenon in the largest ("signal") eigenvalue is derived using heuristic arguments. This exposes a fundamental limit on the identifiability of low-level signals due to sample size constraints when using the sample eigenvalues alone. The analysis is extended to address a problem in sensor array processing, posed by Baggeroer and Cox, on the distribution of the outputs of the Capon-MVDR beamformer when the sample covariance matrix is diagonally loaded.<br>(cont.) The second part of the dissertation investigates the limiting distribution of the eigenvalues and eigenvectors of a broader class of random matrices. A powerful method is proposed that expands the reach of the theory beyond the special cases of matrices with Gaussian entries; this simultaneously establishes a framework for computational (non-commutative) "free probability" theory. The class of "algebraic" random matrices is defined and the generators of this class are specified. Algebraicity of a random matrix sequence is shown to act as a certificate of the computability of the limiting eigenvalue distribution and, for a subclass, the limiting conditional "eigenvector distribution." The limiting moments of algebraic random matrix sequences, when they exist, are shown to satisfy a finite depth linear recursion so that they may often be efficiently enumerated in closed form. The method is applied to predict the deterioration in the quality of the sample eigenvectors of large algebraic empirical covariance matrices due to sample size constraints.<br>by Rajesh Rao Nadakuditi.<br>Ph.D.
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Liu, Xuan. "Some contribution to analysis and stochastic analysis." Thesis, University of Oxford, 2018. http://ora.ox.ac.uk/objects/uuid:485474c0-2501-4ef0-a0bc-492e5c6c9d62.

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The dissertation consists of two parts. The first part (Chapter 1 to 4) is on some contributions to the development of a non-linear analysis on the quintessential fractal set Sierpinski gasket and its probabilistic interpretation. The second part (Chapter 5) is on the asymptotic tail decays for suprema of stochastic processes satisfying certain conditional increment controls. Chapters 1, 2 and 3 are devoted to the establishment of a theory of backward problems for non-linear stochastic differential equations on the gasket, and to derive a probabilistic representation to some parabolic type partial differential equations on the gasket. In Chapter 2, using the theory of Markov processes, we derive the existence and uniqueness of solutions to backward stochastic differential equations driven by Brownian motion on the Sierpinski gasket, for which the major technical difficulty is the exponential integrability of quadratic processes of martingale additive functionals. A Feynman-Kac type representation is obtained as an application. In Chapter 3, we study the stochastic optimal control problems for which the system uncertainties come from Brownian motion on the gasket, and derive a stochastic maximum principle. It turns out that the necessary condition for optimal control problems on the gasket consists of two equations, in contrast to the classical result on &Ropf;<sup>d</sup>, where the necessary condition is given by a single equation. The materials in Chapter 2 are based on a joint work with Zhongmin Qian (referenced in Chapter 2). Chapter 4 is devoted to the analytic study of some parabolic PDEs on the gasket. Using a new type of Sobolev inequality which involves singular measures developed in Section 4.2, we establish the existence and uniqueness of solutions to these PDEs, and derive the space-time regularity for solutions. As an interesting application of the results in Chapter 4 and the probabilistic representation developed in Chapter 2, we further study Burgers equations on the gasket, to which the space-time regularity for solutions is deduced. The materials in Chapter 4 are based on a joint work with Zhongmin Qian (referenced in Chapter 4). In Chapter 5, we consider a class of continuous stochastic processes which satisfy the conditional increment control condition. Typical examples include continuous martingales, fractional Brownian motions, and diffusions governed by SDEs. For such processes, we establish a Doob type maximal inequality. Under additional assumptions on the tail decays of their marginal distributions, we derive an estimate for the tail decay of the suprema (Theorem 5.3.2), which states that the suprema decays in a manner similar to the margins of the processes. In Section 5.4, as an application of Theorem 5.3.2, we derive the existence of strong solutions to a class of SDEs. The materials in this chapter is based on the work [44] by the author (Section 5.2 and Section 5.3) and an ongoing joint project with Guangyu Xi (Section 5.4).
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Johannessen, Knut. "Stochastic analysis of Workover Risers." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for marin teknikk, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-11550.

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The goal of this thesis is to investigate the properties of dynamic analysis of slender, top tensioned risers when using both regular and irregular waves. The goal was to discover properties in the response that correlates to a parameter that can be found in both methods. The approach is to use a model of a riser in open water connected to the sea bed, and top tensioned by a semi submersible rig subjected to the dynamic load of waves and currents. The theories that hold the basis of dynamic analyses using finite elements are outlined, and different methods of solving the dynamic equilibrium equation are discussed. Relevant wave theories and their statistical properties are investigated and outlined, followed by a clear methodology for performing the case study. The data from the case study is based on a large number of irregular analyses, and one regular wave analysis for each sea state. The extreme values is extracted from the irregular simulations and fitted to a Gumbel extreme value distribution. The distributions are extrapolated to return periods of 1, 10 and 100 years, and compared to the extreme values from the regular wave analyses. The main results from the case study are: - The extreme response of an irregular simulation does not seem to correlate with the largest wave height in the simulation. - The bending moment seems to correlate weakly to the displacement and velocity of the rig. - The bending moment seems to correlate well with the displacement of the riser pipe. More in surge and pitch than in heave. - The bending moment seems to correlate well with the velocity of the riser pipe. More in surge and pitch than in heave. The results from the case study are used in a discussion on how to fine tune a regular wave analysis to be used in a consistent way to define the safe operation limitations for a top tensioned work over riser.
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Youssef, Nataly. "Stochastic analysis via robust optimization." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/103246.

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Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2016.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 167-174).<br>To evaluate the performance and optimize systems under uncertainty, two main avenues have been suggested in the literature: stochastic analysis and optimization describing the uncertainty probabilistically and robust optimization describing the uncertainty deterministically. Instead, we propose a novel paradigm which leverages the conclusions of probability theory and the tractability of the robust optimization approach to approximate and optimize the expected behavior in a given system. Our framework models the uncertainty via polyhedral sets inspired by the limit laws of probability. We characterize the uncertainty sets by variability parameters that we treat as random variables. We then devise a methodology to approximate and optimize the average performance of the system via a robust optimization formulation. Our framework (a) avoids the challenges of fitting probability distributions to the uncertain variables, (b) eliminates the need to generate scenarios to describe the states of randomness, and (c) demonstrates the use of robust optimization to evaluate and optimize expected performance. We illustrate the applicability of our methodology to analyze the performance of queueing networks and optimize the inventory policy for supply chain networks. In Part I, we study the case of a single queue. We develop a robust theory to study multi-server queues with possibly heavy-tailed primitives. Our methodology (a) provides approximations that match the diffusion approximations for light-tailed queues in heavy traffic, and (b) extends the framework to analyze the transient behavior of heavy-tailed queues. In Part II, we study the case of a network of queues. Our methodology provides accurate approximations of (a) the expected steady-state behavior in generalized queueing networks, and (b) the expected transient behavior in feedforward queueing networks. Our approach achieves significant computational tractability and provides accurate approximations relative to simulated values. In Part III, we study the case of a supply chain network. Our methodology (a) obtains optimal base-stock levels that match the optimal solutions obtained via stochastic optimization, (b) yields optimal affine policies which oftentimes exhibit better results compared to optimal base-stock policies, and (c) provides optimal policies that consistently outperform the solutions obtained via the traditional robust optimization approach.<br>by Nataly Youssef.<br>Ph. D.
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Whiteside, M. B. "Stochastic analysis of composite materials." Thesis, Imperial College London, 2012. http://hdl.handle.net/10044/1/9986.

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This thesis describes the development of stochastic analysis frameworks for use in engineering design and optimisation. The research focuses on fibre-reinforced composites, with the stochastic analyses of an existing analytical failure model for unidirectional composites and of a unit cell numerical model of a 2D 5-Harness satin weave. Stochastic failure envelopes are generated through parallelised Monte Carlo Simulation of deterministic, analytical, physically based failure criteria for unidirectional carbon fibre/epoxy matrix composite plies. Monte Carlo integration of global variance-based Sobol sensitivity indices is performed and utilised to decompose observed variance within stochastic failure envelopes into contributions from physical input parameters. It is observed how the interaction effect can be used to identify domains of bi-modal failure, within which the predicted failure probability is governed by multiple failure modes. A reduced unit cell (rUC) model of a 5-Harness satin weave is constructed and analysed deterministically in uniaxial and biaxial loading conditions. An algorithm is developed and implemented to fully automate the rUC construction such that stochastic variations of the crimp angle can be evaluated. Monte Carlo Simulation is employed to propagate the effect of the crimp angle through the deterministic model and the probabilistic response compared with data obtained experimentally. It is observed how simulated variability compares well in uni-axial compression, but under-predicts observed experimental variability in uni-axial tension. The influence of vertical stacking sequence of plies is also demonstrated through the study of in-phase and out-of-phase periodic boundary conditions. The research highlights various, potential advantages that stochastic methodologies offer over the traditional deterministic approach, making a case for their application in engineering design and providing a springboard for further research come the day when greater computational power is available.
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Güngör, Mesut Savacı Ferit Acar. "Analysis of Stochastic Dynamical Systems/." [s.l.]: [s.n.], 2007. http://library.iyte.edu.tr/tezler/master/elektrikveelektronikmuh/T000630.pdf.

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Libros sobre el tema "Stochastic analysis"

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Métivier, Michel, and Shinzo Watanabe, eds. Stochastic Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/bfb0077861.

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Cranston, Michael, and Mark Pinsky, eds. Stochastic Analysis. Providence, Rhode Island: American Mathematical Society, 1994. http://dx.doi.org/10.1090/pspum/057.

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Kusuoka, Shigeo. Stochastic Analysis. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8864-8.

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Malliavin, Paul. Stochastic Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-15074-6.

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Malliavin, Paul. Stochastic analysis. Berlin: Springer, 1997.

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Karatzas, Ioannis, and Daniel Ocone, eds. Applied Stochastic Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/bfb0007043.

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Crisan, Dan, ed. Stochastic Analysis 2010. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-15358-7.

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A, Davis M. H., and Elliott Robert J. 1940-, eds. Applied stochastic analysis. New York: Gordon and Breach Science Publishers, 1991.

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A, Davis M. H., and Elliott R. J, eds. Applied stochastic analysis. New York: Gordon and Breach, 1990.

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service), SpringerLink (Online, ed. Stochastic Analysis 2010. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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Capítulos de libros sobre el tema "Stochastic analysis"

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Osswald, Horst. "Stochastic Analysis." In Nonstandard Analysis for the Working Mathematician, 233–319. Dordrecht: Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-7327-0_7.

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Hacιsalihzade, Selim S. "Stochastic Analysis." In Control Engineering and Finance, 139–80. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64492-9_5.

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Natale, Marco Di, Haibo Zeng, Paolo Giusto, and Arkadeb Ghosal. "Stochastic Analysis." In Understanding and Using the Controller Area Network Communication Protocol, 67–88. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0314-2_4.

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Ganguli, Ranjan, Sondipon Adhikari, Souvik Chakraborty, and Mrittika Ganguli. "Stochastic Analysis." In Digital Twin, 83–90. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003268048-4.

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Karim, Md Rezaul, and M. Ataharul Islam. "Stochastic Models." In Reliability and Survival Analysis, 197–218. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9776-9_11.

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Hu, Shouchuan, and Nikolas S. Papageorgiou. "Stochastic Games." In Handbook of Multivalued Analysis, 705–90. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4665-8_7.

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Wen, Meilin. "Stochastic DEA." In Uncertain Data Envelopment Analysis, 61–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-43802-2_3.

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Söderström, T. "Analysis." In Discrete-time Stochastic Systems, 59–122. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0101-7_4.

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Jacob, Christian. "Stochastic Search Methods." In Intelligent Data Analysis, 299–350. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03969-4_9.

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Au, Siu-Kui. "Stochastic Structural Dynamics." In Operational Modal Analysis, 179–204. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4118-1_5.

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Actas de conferencias sobre el tema "Stochastic analysis"

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Jiang, Shanshan, Lijin Wang, and Jialin Hong. "Stochastic multisymplectic integrator for stochastic KdV equation." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756515.

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"Stochastic analysis." In Proceedings of the 7th International ISAAC Congress. WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789814313179_others10.

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Kanniainen, Juho. "Cause of Stock Return Stochastic Volatility: Query by Way of Stochastic Calculus." In Recent Advances in Stochastic Modeling and Data Analysis. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709691_0003.

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Ekhaguere, G. O. S. "Contemporary Stochastic Analysis." In International Conference on Contemporary Problems in Stochastic Analysis and its Applications. WORLD SCIENTIFIC, 1991. http://dx.doi.org/10.1142/9789814538756.

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Negrea, Romeo. "On a class of backward stochastic differential equations and applications to the stochastic resonance." In Recent Advances in Stochastic Modeling and Data Analysis. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709691_0004.

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Hong, Jialin, and Lihai Ji. "Stochastic multi-symplectic wavelet collocation method for stochastic Hamiltonian Maxwell's equations." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756514.

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P., Spanos, Pirrotta A., Marino F., and Robledo Ricardo L. A. "Stochastic Analysis of Motorcycle Dynamics." In 6th International Conference on Computational Stochastic Mechanics. Singapore: Research Publishing Services, 2011. http://dx.doi.org/10.3850/978-981-08-7619-7_p056.

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Juuti, Mika, Francesco Corona, and Juha Karhunen. "Stochastic Discriminant Analysis." In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280609.

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Pettere, Gaida. "Stochastic Risk Capital Model for Insurance Company." In Recent Advances in Stochastic Modeling and Data Analysis. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709691_0014.

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Wisniewski, Rafael, and Manuela L. Bujorianu. "Stochastic safety analysis of stochastic hybrid systems." In 2017 IEEE 56th Annual Conference on Decision and Control (CDC). IEEE, 2017. http://dx.doi.org/10.1109/cdc.2017.8263999.

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Informes sobre el tema "Stochastic analysis"

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Cawlfield, J. D. Stochastic analysis of contaminant transport. Office of Scientific and Technical Information (OSTI), February 1992. http://dx.doi.org/10.2172/5827751.

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Budhiraja, Amarjit. Stochastic Analysis and Applied Probability(3.3.1): Topics in the Theory and Applications of Stochastic Analysis. Fort Belvoir, VA: Defense Technical Information Center, July 2015. http://dx.doi.org/10.21236/ada625850.

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HEY, B. E. Stochastic Consequence Analysis for Waste Leaks. Office of Scientific and Technical Information (OSTI), May 2000. http://dx.doi.org/10.2172/803657.

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Johnson, Ralph. Stochastic Simulation Analysis - 2005 (SSA-05). Fort Belvoir, VA: Defense Technical Information Center, July 1997. http://dx.doi.org/10.21236/ada329429.

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Mathew, George A., and Alessandro Pinto. Stochastic Analysis and Design of Systems. Fort Belvoir, VA: Defense Technical Information Center, September 2011. http://dx.doi.org/10.21236/ada552645.

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Heifets, Samuel A. Quantum-mechanical Analysis of Optical Stochastic Cooling. Office of Scientific and Technical Information (OSTI), November 2000. http://dx.doi.org/10.2172/784790.

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Heifets, Samuel A. Quantum-mechanical Analysis of Optical Stochastic Cooling. Office of Scientific and Technical Information (OSTI), November 2000. http://dx.doi.org/10.2172/784820.

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Dshalalow, Jewgeni H. Random Walk Analysis in Antagonistic Stochastic Games. Fort Belvoir, VA: Defense Technical Information Center, July 2010. http://dx.doi.org/10.21236/ada533481.

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Foes, Chamberlain. A study an analysis of stochastic linear programming. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.821.

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Ghassemi, Ahmad. Geomechanics-Based Stochastic Analysis of Injection- Induced Seismicity. Office of Scientific and Technical Information (OSTI), August 2017. http://dx.doi.org/10.2172/1375732.

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