Uncertainty Quantification for Stochastic Dynamical Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Uncertainty Quantification for Stochastic Dynamical Systems write by Michael Schick. This book was released on 2011. Uncertainty Quantification for Stochastic Dynamical Systems available in PDF, EPUB and Kindle.
Uncertainty Quantification of Dynamical Systems and Stochastic Symplectic Schemes
Uncertainty Quantification of Dynamical Systems and Stochastic Symplectic Schemes - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Uncertainty Quantification of Dynamical Systems and Stochastic Symplectic Schemes write by Jian Deng. This book was released on 2013. Uncertainty Quantification of Dynamical Systems and Stochastic Symplectic Schemes available in PDF, EPUB and Kindle.
New Algorithms for Uncertainty Quantification and Nonlinear Estimation of Stochastic Dynamical Systems
New Algorithms for Uncertainty Quantification and Nonlinear Estimation of Stochastic Dynamical Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook New Algorithms for Uncertainty Quantification and Nonlinear Estimation of Stochastic Dynamical Systems write by Parikshit Dutta. This book was released on 2012. New Algorithms for Uncertainty Quantification and Nonlinear Estimation of Stochastic Dynamical Systems available in PDF, EPUB and Kindle. Recently there has been growing interest to characterize and reduce uncertainty in stochastic dynamical systems. This drive arises out of need to manage uncertainty in complex, high dimensional physical systems. Traditional techniques of uncertainty quantification (UQ) use local linearization of dynamics and assumes Gaussian probability evolution. But several difficulties arise when these UQ models are applied to real world problems, which, generally are nonlinear in nature. Hence, to improve performance, robust algorithms, which can work efficiently in a nonlinear non-Gaussian setting are desired. The main focus of this dissertation is to develop UQ algorithms for nonlinear systems, where uncertainty evolves in a non-Gaussian manner. The algorithms developed are then applied to state estimation of real-world systems. The first part of the dissertation focuses on using polynomial chaos (PC) for uncertainty propagation, and then achieving the estimation task by the use of higher order moment updates and Bayes rule. The second part mainly deals with Frobenius-Perron (FP) operator theory, how it can be used to propagate uncertainty in dynamical systems, and then using it to estimate states by the use of Bayesian update. Finally, a method to represent the process noise in a stochastic dynamical system using a nite term Karhunen-Loeve (KL) expansion is proposed. The uncertainty in the resulting approximated system is propagated using FP operator. The performance of the PC based estimation algorithms were compared with extended Kalman filter (EKF) and unscented Kalman filter (UKF), and the FP operator based techniques were compared with particle filters, when applied to a duffing oscillator system and hypersonic reentry of a vehicle in the atmosphere of Mars. It was found that the accuracy of the PC based estimators is higher than EKF or UKF and the FP operator based estimators were computationally superior to the particle filtering algorithms.
Stochastic Systems
Stochastic Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Stochastic Systems write by Mircea Grigoriu. This book was released on 2012-05-15. Stochastic Systems available in PDF, EPUB and Kindle. Uncertainty is an inherent feature of both properties of physical systems and the inputs to these systems that needs to be quantified for cost effective and reliable designs. The states of these systems satisfy equations with random entries, referred to as stochastic equations, so that they are random functions of time and/or space. The solution of stochastic equations poses notable technical difficulties that are frequently circumvented by heuristic assumptions at the expense of accuracy and rigor. The main objective of Stochastic Systems is to promoting the development of accurate and efficient methods for solving stochastic equations and to foster interactions between engineers, scientists, and mathematicians. To achieve these objectives Stochastic Systems presents: A clear and brief review of essential concepts on probability theory, random functions, stochastic calculus, Monte Carlo simulation, and functional analysis Probabilistic models for random variables and functions needed to formulate stochastic equations describing realistic problems in engineering and applied sciences Practical methods for quantifying the uncertain parameters in the definition of stochastic equations, solving approximately these equations, and assessing the accuracy of approximate solutions Stochastic Systems provides key information for researchers, graduate students, and engineers who are interested in the formulation and solution of stochastic problems encountered in a broad range of disciplines. Numerous examples are used to clarify and illustrate theoretical concepts and methods for solving stochastic equations. The extensive bibliography and index at the end of the book constitute an ideal resource for both theoreticians and practitioners.
Proceedings of the 5th International Symposium on Uncertainty Quantification and Stochastic Modelling
Proceedings of the 5th International Symposium on Uncertainty Quantification and Stochastic Modelling - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Proceedings of the 5th International Symposium on Uncertainty Quantification and Stochastic Modelling write by José Eduardo Souza De Cursi. This book was released on 2020-08-19. Proceedings of the 5th International Symposium on Uncertainty Quantification and Stochastic Modelling available in PDF, EPUB and Kindle. This proceedings book discusses state-of-the-art research on uncertainty quantification in mechanical engineering, including statistical data concerning the entries and parameters of a system to produce statistical data on the outputs of the system. It is based on papers presented at Uncertainties 2020, a workshop organized on behalf of the Scientific Committee on Uncertainty in Mechanics (Mécanique et Incertain) of the AFM (French Society of Mechanical Sciences), the Scientific Committee on Stochastic Modeling and Uncertainty Quantification of the ABCM (Brazilian Society of Mechanical Sciences) and the SBMAC (Brazilian Society of Applied Mathematics).
