Thèses sur le sujet « Stochastic accelerations »
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Fossà, Alberto. « Propagation multi-fidélité d’incertitude orbitale en présence d’accélérations stochastiques ». Electronic Thesis or Diss., Toulouse, ISAE, 2024. http://www.theses.fr/2024ESAE0009.
Texte intégralThe problem of nonlinear uncertainty propagation (UP) is crucial in astrodynamics since all systems of practical interest, ranging from navigation to orbit determination (OD) and target tracking, involve nonlinearities in their dynamics and measurement models. One topic of interest is the accurate propagation of uncertainty through the nonlinear orbital dynamics, a fundamental requirement in several applications such as space surveillance and tracking (SST), space traffic management (STM), and end-of-life (EOL) disposal. Given a finite-dimensional representation of the probability density function (pdf) of the initial state, the main goal is to obtain a similar representation of the state pdf at any future time. This problem has been historically tackled with either linearized methods or Monte Carlo (MC) simulations, both of which are unsuitable to satisfy the demand of a rapidly growing number of applications. Linearized methods are light on computational resources, but cannot handle strong nonlinearities or long propagation windows due to the local validity of the linearization. In contrast, MC methods can handle any kind of nonlinearity, but are too computationally expensive for any task that requires the propagation of several pdfs. Instead, this thesis leverages multifidelity methods and differential algebra (DA) techniques to develop computationally efficient methods for the accurate propagation of uncertainties through nonlinear dynamical systems. The first method, named low-order automatic domain splitting (LOADS), represents the uncertainty with a set of second-order Taylor polynomials and leverages a DA-based measure of nonlinearity to adjust their number based on the local dynamics and the required accuracy. An adaptive Gaussian mixture model (GMM) method is then developed by associating each polynomial to a weighted Gaussian kernel, thus obtaining an analytical representation of the state pdf. Going further, a multifidelity method is proposed to reduce the computational cost of the former algorithms while retaining a similar accuracy. The adaptive GMM method is in this case run on a low-fidelity dynamical model, and only the expected values of the kernels are propagated point-wise in high-fidelity dynamics to compute a posteriori correction of the low-fidelity state pdf. If the former methods deal with the propagation of an initial uncertainty through a deterministic dynamical model, the effects of mismodeled or unmodeled forces are finally considered to further enhance the realism of the propagated statistics. In this case, the multifidelity GMM method is used at first to propagate the initial uncertainty through a low-fidelity, deterministic dynamical model. The point-wise propagations are then replaced with a DA-based algorithm to efficiently propagate a polynomial representation of the moments of the pdf in a stochastic dynamical system. These moments model the effects of stochastic accelerations on the deterministic kernels’ means, and coupled with the former GMM provide a description of the propagated state pdf that accounts for both the uncertainty in the initial state and the effects of neglected forces. The proposed methods are applied to the problem of orbit UP, and their performance is assessed in different orbital regimes. The results demonstrate the effectiveness of these methods in accurately propagating the initial uncertainty and the effects of process noise at a fraction of the computational cost of high-fidelity MC simulations. The LOADS method is then employed to solve the initial orbit determination (IOD) problem by exploiting the information on measurement uncertainty and to develop a preprocessing scheme aimed at improving the robustness of batch OD algorithms. These tools are finally validated on a set of real observations for an object in geostationary transfer orbit (GTO)
Wolf, Christian [Verfasser]. « Advanced acceleration techniques for Nested Benders decomposition in stochastic programming / Christian Wolf ». Paderborn : Universitätsbibliothek, 2014. http://d-nb.info/1046905090/34.
Texte intégralMcEvoy, Erica L., et Erica L. McEvoy. « A Numerical Method for the Simulation of Skew Brownian Motion and its Application to Diffusive Shock Acceleration of Charged Particles ». Diss., The University of Arizona, 2017. http://hdl.handle.net/10150/625664.
Texte intégralWilhelm, Alina [Verfasser], Martin [Akademischer Betreuer] Pohl, Christoph [Gutachter] Pfrommer et Julia [Gutachter] Tjus. « Stochastic re-acceleration of particles in supernova remnants / Alina Wilhelm ; Gutachter : Christoph Pfrommer, Julia Tjus ; Betreuer : Martin Pohl ». Potsdam : Universität Potsdam, 2021. http://d-nb.info/123972909X/34.
Texte intégralTrimeloni, Thomas. « Accelerating Finite State Projection through General Purpose Graphics Processing ». VCU Scholars Compass, 2011. http://scholarscompass.vcu.edu/etd/175.
Texte intégralHall, 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.
Texte intégralSANTOS, FELIPE SILVA PLACIDO DOS. « ACCELERATING BENDERS STOCHASTIC DECOMPOSITION FOR THE OPTIMIZATION OF PARTIAL BACKORDER CONTROL FOR PERIODIC REVIEW (R, S) INVENTORY SYSTEM WITH UNCERTAIN DEMAND ». PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2016. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=31326@1.
Texte intégralINSTITUTO MILITAR DE ENGENHARIA
CENTRO TECNOLÓGICO DO EXÉRCITO
Este trabalho apresenta uma proposta de aceleração da decomposição de Benders aplicada a uma versão mais geral e compacta (menos restrições e variáveis) do modelo de gestão de estoques, otimizado via programação estocástica de dois estágios que considera uma camada, um item, demanda incerta e política de controle (R, S). De maneira a ser possível considerar problemas de grande porte, foram aplicados os métodos L-Shaped tradicional com corte único e a sua forma estendida com múltiplos cortes. Resultados computacionais preliminares mostraram um substancial melhor desempenho computacional do método L-Shaped tradicional em relação à sua forma multi-cut L-Shaped, mesmo o primeiro necessitando de mais iterações para convergir na solução ótima. Tal observação motivou o desenvolvimento de uma nova técnica de aceleração da decomposição de Benders e de um conjunto de desigualdades válidas. Experimentos numéricos mostram que a abordagem proposta de combinar a técnica de aceleração elaborada com as desigualdades válidas desenvolvidas provê significativa redução do tempo computacional necessário para a solução de instâncias de grande porte.
This dissertation presents a speed up proposal for the Benders decomposition applied to a more general and compact version (less constraints and variables) of inventory management model, optimized via two-stage stochastic programming, which considers one layer, one item, uncertain demand and control policy (R, S). In order to be possible to consider large scale problems, the L-Shaped traditional method with single cuts and its extended form with multiple cuts were applied. Preliminary computational results showed a substantially better computational performance of the traditional L-Shaped method in comparison to the multi-cut L-Shaped method, even with the first requiring more iterations to converge on optimum solutions. This observation led to the development of a new technique to accelerate the decomposition of Benders and a set of valid inequalities. Numerical experiments show that the proposed approach of combining the elaborate acceleration technique with the developed valid inequalities, provide significant reduction in the computational time required to solve large scale instances.
Flammarion, Nicolas. « Stochastic approximation and least-squares regression, with applications to machine learning ». Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEE056/document.
Texte intégralMany problems in machine learning are naturally cast as the minimization of a smooth function defined on a Euclidean space. For supervised learning, this includes least-squares regression and logistic regression. While small problems are efficiently solved by classical optimization algorithms, large-scale problems are typically solved with first-order techniques based on gradient descent. In this manuscript, we consider the particular case of the quadratic loss. In the first part, we are interestedin its minimization when its gradients are only accessible through a stochastic oracle. In the second part, we consider two applications of the quadratic loss in machine learning: clustering and estimation with shape constraints. In the first main contribution, we provided a unified framework for optimizing non-strongly convex quadratic functions, which encompasses accelerated gradient descent and averaged gradient descent. This new framework suggests an alternative algorithm that exhibits the positive behavior of both averaging and acceleration. The second main contribution aims at obtaining the optimal prediction error rates for least-squares regression, both in terms of dependence on the noise of the problem and of forgetting the initial conditions. Our new algorithm rests upon averaged accelerated gradient descent. The third main contribution deals with minimization of composite objective functions composed of the expectation of quadratic functions and a convex function. Weextend earlier results on least-squares regression to any regularizer and any geometry represented by a Bregman divergence. As a fourth contribution, we consider the the discriminative clustering framework. We propose its first theoretical analysis, a novel sparse extension, a natural extension for the multi-label scenario and an efficient iterative algorithm with better running-time complexity than existing methods. The fifth main contribution deals with the seriation problem. We propose a statistical approach to this problem where the matrix is observed with noise and study the corresponding minimax rate of estimation. We also suggest a computationally efficient estimator whose performance is studied both theoretically and experimentally
Kulunchakov, Andrei. « Optimisation stochastique pour l'apprentissage machine à grande échelle : réduction de la variance et accélération ». Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALM057.
Texte intégralA goal of this thesis is to explore several topics in optimization for high-dimensional stochastic problems. The first task is related to various incremental approaches, which rely on exact gradient information, such as SVRG, SAGA, MISO, SDCA. While the minimization of large limit sums of functions was thoroughly analyzed, we suggest in Chapter 2 a new technique, which allows to consider all these methods in a generic fashion and demonstrate their robustness to possible stochastic perturbations in the gradient information.Our technique is based on extending the concept of estimate sequence introduced originally by Yu. Nesterov in order to accelerate deterministic algorithms.Using the finite-sum structure of the problems, we are able to modify the aforementioned algorithms to take into account stochastic perturbations. At the same time, the framework allows to derive naturally new algorithms with the same guarantees as existing incremental methods. Finally, we propose a new accelerated stochastic gradient descent algorithm and a new accelerated SVRG algorithm that is robust to stochastic noise. This acceleration essentially performs the typical deterministic acceleration in the sense of Nesterov, while preserving the optimal variance convergence.Next, we address the problem of generic acceleration in stochastic optimization. For this task, we generalize in Chapter 3 the multi-stage approach called Catalyst, which was originally aimed to accelerate deterministic methods. In order to apply it to stochastic problems, we improve its flexibility on the choice of surrogate functions minimized at each stage. Finally, given an optimization method with mild convergence guarantees for strongly convex problems, our developed multi-stage procedure, accelerates convergence to a noise-dominated region, and then achieves the optimal (up to a logarithmic factor) worst-case convergence depending on the noise variance of the gradients. Thus, we successfully address the acceleration of various stochastic methods, including the variance-reduced approaches considered and generalized in Chapter 2. Again, the developed framework bears similarities with the acceleration performed by Yu. Nesterov using the estimate sequences. In this sense, we try to fill the gap between deterministic and stochastic optimization in terms of Nesterov's acceleration. A side contribution of this chapter is a generic analysis that can handle inexact proximal operators, providing new insights about the robustness of stochastic algorithms when the proximal operator cannot be exactly computed.In Chapter 4, we study properties of non-Euclidean stochastic algorithms applied to the problem of sparse signal recovery. A sparse structure significantly reduces the effects of noise in gradient observations. We propose a new stochastic algorithm, called SMD-SR, allowing to make better use of this structure. This method is a multi-step procedure which uses the stochastic mirror descent algorithm as a building block over its stages. Essentially, SMD-SR has two phases of convergence with the linear bias convergence during the preliminary phase and the optimal asymptotic rate during the asymptotic phase.Comparing to the most effective existing solution to the sparse stochastic optimization problems, we offer an improvement in several aspects. First, we establish the linear bias convergence (similar to the one of the deterministic gradient descent algorithm, when the full gradient observation is available), while showing the optimal robustness to noise. Second, we achieve this rate for a large class of noise models, including sub-Gaussian, Rademacher, multivariate Student distributions and scale mixtures. Finally, these results are obtained under the optimal condition on the level of sparsity which can approach the total number of iterations of the algorithm (up to a logarithmic factor)
Rassou, Sébastien. « Accélération d'électrons par onde de sillage laser : Développement d’un modèle analytique étendu au cas d’un plasma magnétisé dans le régime du Blowout ». Thesis, Université Paris-Saclay (ComUE), 2015. http://www.theses.fr/2015SACLS066/document.
Texte intégralAn intense laser pulse propagating in an under dense plasma (ne< 10¹⁸ W.cm⁻²) and short(τ₀< 100 fs), the bubble regime is reached. Within the bubble the electric field can exceed 100 GV/m and a trapped electron beam is accelerated to GeV energy with few centimetres of plasma.In this regime, the electrons expelled by the laser ponderomotive force are brought back and form a dense sheath layer. First, an analytic model was derived using W. Lu and S. Yi formalisms in order to investigate the properties of the wakefield in the blowout regime. In a second part, the trapping and injection mechanisms into the wakefield were studied. When the optical injection scheme is used, electrons may undergo stochastic heating or cold injection depending on the lasers’ polarisations. A similarity parameter was introduced to find out the most appropriate method to maximise the trapped charge. In a third part, our analytic model is extended to investigate the influence of an initially applied longitudinal magnetic field on the laser wakefield in the bubble regime. When the plasma is magnetized two remarkable phenomena occur. Firstly the bubble is opened at its rear, and secondly the longitudinal magnetic field is amplified - at the rear of the bubble - due to the azimuthal current induced by the variation of the magnetic flux. The predictions of our analytic model were shown to be in agreement with 3D PIC simulation results obtained with Calder-Circ. In most situations the wake shape is altered and self-injection can be reduced or even cancelled by the applied magnetic field. However, the application of a longitudinal magnetic field, combined with a careful choice of laser-plasma parameters, reduces the energy spread of the electron beam produced after optical injection
Barge, Alexis. « Propriétés lagrangiennes de l'accélération turbulente des particules fluides et inertielles dans un écoulement avec un cisaillement homogène : DNS et nouveaux modèles de sous-maille de LES ». Thesis, Lyon, 2018. http://www.theses.fr/2018LYSEC012/document.
Texte intégralThe main objective of this thesis is to study the acceleration of fluid and inertial particles moving in a turbulent flow under the influence of a homogeneous shear in order to develop LES stochastic models that predict subgrid acceleration of the flow and acceleration of inertial particles. Subgrid acceleration modelisation is done in the framework of the LES-SSAM approach which was introduced by Sabel’nikov, Chtab and Gorokhovski[EPJB 80:177]. Acceleration is predicted with two independant stochastic models : a log-normal Ornstein-Uhlenbeck process for the norm of acceleration and an Ornstein-Uhlenbeck process expressed in the sense of Stratonovich calculus for the components of the acceleration orientation vector. The approach is used to simulate fluid and inertial particles moving in a homogeneous isotropic turbulence and in a homogeneous sheared turbulence. Our results show that small scales statistics of particles are better predicted in comparison with classical LES approach. Modelling of inertial particles acceleration is done in the framework of the LES-STRIP which was introduced by Gorokhovski and Zamansky[PRF 3:034602] with two independant stochastic models in a similar way to the subgrid fluid acceleration. Computations of inertial particles in the homogeneous shear flow present good predicitons of the particles acceleration and velocity when STRIP model is used. In the last chapter, we present an equation to describe the dynamic of point-like particles which size is larger than the Kolmogorov scale moving in a homogeneous isotropic turbulence computed by direct numerical simulation. Results are compared with experiments and indicate that this description reproduces well the properties of the particles dynamic
Zamansky, Rémi. « Simulation numérique directe et modélisation stochastique de sous-maille de l'accélération dans un écoulement de canal à grand nombre de Reynolds ». Phd thesis, Ecole Centrale de Lyon, 2011. http://tel.archives-ouvertes.fr/tel-00673464.
Texte intégralMcCollum, James Michael. « Accelerating exact stochastic simulation ». 2004. http://etd.utk.edu/2004/McCollumJamesMichael.pdf.
Texte intégralTitle from title page screen (viewed May 20, 2004). Thesis advisor: Gregory D. Peterson. Document formatted into pages (x, 148 p. : ill. (some col.)). Vita. Includes bibliographical references (p. 85-87).
Thurmon, Brandon Parks. « Reconfigurable hardware acceleration of exact stochastic simulation ». 2005. http://etd.utk.edu/2005/ThurmonBrandon.pdf.
Texte intégralTitle from title page screen (viewed on Sept. 1, 2005). Thesis advisor: Gregory D. Peterson. Document formatted into pages (viii, 218 p. : ill. (some color)). Vita. Includes bibliographical references (p. 67-69).
McCollum, James Michael. « Accelerating exact stochastic simulation of biochemical systems ». 2006. http://etd.utk.edu/2006/McCollumJamesMichael.pdf.
Texte intégralTitle from title page screen (viewed on Sept. 15, 2006). Thesis advisor: Gregory D. Peterson. Vita. Includes bibliographical references.
Jenkins, David Dewayne. « Accelerating the Stochastic Simulation Algorithm Using Emerging Architectures ». 2009. http://trace.tennessee.edu/utk_gradthes/533.
Texte intégralShen, Jie Pan, et 沈介磐. « The study of stochastic charged particle acceleration by electromagnetic waves in the uniform magnetic field ». Thesis, 1995. http://ndltd.ncl.edu.tw/handle/68809201774250171302.
Texte intégral國立清華大學
核子工程學系
83
A general relativistic Hamiltonian formalism is established including the consideration of several mixture electromagnetic and electrostatic waves propagating obliquely to an external, uniform, static magnetic field. The mechanism of unlimited particle aceleration is explained and relativistic effect is proved to play a crucial role, which determins the topology of Hamiltonian surface. Both accidentally and intrinsically degenerate cases are thoroughly considered. The stochasticity threshold based on overlap criterion is given. An emphasis on instrinsic degeneracy is discussed. Especially, the well-know autoresonance mechanism is shown to be a special case of intrinsic degeneracy. Finally, an application of intrinsic degeneracy to a novel enhanced acceleration mechanism in two waves is asserted and several numerical results are present.
ALDERUCCI, TIZIANA. « Methods for the analysis of structural systems subjected to seismic acceleration modelled as stochastic processes ». Doctoral thesis, 2017. http://hdl.handle.net/11570/3105309.
Texte intégral