Uncertainty Quantification and Stochastic Modeling with Matlab

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  • Publisher : Elsevier
  • Release : 09 April 2015
  • ISBN : 9780081004715
  • Page : 456 pages
  • Rating : 4.5/5 from 103 voters

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Uncertainty Quantification (UQ) is a relatively new research area which describes the methods and approaches used to supply quantitative descriptions of the effects of uncertainty, variability and errors in simulation problems and models. It is rapidly becoming a field of increasing importance, with many real-world applications within statistics, mathematics, probability and engineering, but also within the natural sciences. Literature on the topic has up until now been largely based on polynomial chaos, which raises difficulties when considering different types of approximation and does not lead to a unified presentation of the methods. Moreover, this description does not consider either deterministic problems or infinite dimensional ones. This book gives a unified, practical and comprehensive presentation of the main techniques used for the characterization of the effect of uncertainty on numerical models and on their exploitation in numerical problems. In particular, applications to linear and nonlinear systems of equations, differential equations, optimization and reliability are presented. Applications of stochastic methods to deal with deterministic numerical problems are also discussed. Matlab® illustrates the implementation of these methods and makes the book suitable as a textbook and for self-study. Discusses the main ideas of Stochastic Modeling and Uncertainty Quantification using Functional Analysis Details listings of Matlab® programs implementing the main methods which complete the methodological presentation by a practical implementation Construct your own implementations from provided worked examples

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Uncertainty Quantification and Stochastic Modeling with Matlab

Uncertainty Quantification and Stochastic Modeling with Matlab
  • Author : Eduardo Souza de Cursi,Rubens Sampaio
  • Publisher : Elsevier
  • Release Date : 2015-04-09
  • ISBN : 9780081004715
GET THIS BOOKUncertainty Quantification and Stochastic Modeling with Matlab

Uncertainty Quantification (UQ) is a relatively new research area which describes the methods and approaches used to supply quantitative descriptions of the effects of uncertainty, variability and errors in simulation problems and models. It is rapidly becoming a field of increasing importance, with many real-world applications within statistics, mathematics, probability and engineering, but also within the natural sciences. Literature on the topic has up until now been largely based on polynomial chaos, which raises difficulties when considering different types of

Proceedings of the 5th International Symposium on Uncertainty Quantification and Stochastic Modelling

Proceedings of the 5th International Symposium on Uncertainty Quantification and Stochastic Modelling
  • Author : José Eduardo Souza De Cursi
  • Publisher : Springer Nature
  • Release Date : 2020-08-19
  • ISBN : 9783030536695
GET THIS BOOKProceedings of the 5th International Symposium on Uncertainty Quantification and Stochastic Modelling

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

Optimization of Complex Systems: Theory, Models, Algorithms and Applications

Optimization of Complex Systems: Theory, Models, Algorithms and Applications
  • Author : Hoai An Le Thi,Hoai Minh Le,Tao Pham Dinh
  • Publisher : Springer
  • Release Date : 2019-06-15
  • ISBN : 9783030218034
GET THIS BOOKOptimization of Complex Systems: Theory, Models, Algorithms and Applications

This book contains 112 papers selected from about 250 submissions to the 6th World Congress on Global Optimization (WCGO 2019) which takes place on July 8–10, 2019 at University of Lorraine, Metz, France. The book covers both theoretical and algorithmic aspects of Nonconvex Optimization, as well as its applications to modeling and solving decision problems in various domains. It is composed of 10 parts, each of them deals with either the theory and/or methods in a branch of optimization such as Continuous optimization, DC Programming

Uncertainty Modeling for Engineering Applications

Uncertainty Modeling for Engineering Applications
  • Author : Flavio Canavero
  • Publisher : Springer
  • Release Date : 2018-12-29
  • ISBN : 9783030048709
GET THIS BOOKUncertainty Modeling for Engineering Applications

This book provides an overview of state-of-the-art uncertainty quantification (UQ) methodologies and applications, and covers a wide range of current research, future challenges and applications in various domains, such as aerospace and mechanical applications, structure health and seismic hazard, electromagnetic energy (its impact on systems and humans) and global environmental state change. Written by leading international experts from different fields, the book demonstrates the unifying property of UQ theme that can be profitably adopted to solve problems of different domains.

Computational Intelligence in Emerging Technologies for Engineering Applications

Computational Intelligence in Emerging Technologies for Engineering Applications
  • Author : Orestes Llanes Santiago,Carlos Cruz Corona,Antônio José Silva Neto,José Luis Verdegay
  • Publisher : Springer Nature
  • Release Date : 2020-02-14
  • ISBN : 9783030344092
GET THIS BOOKComputational Intelligence in Emerging Technologies for Engineering Applications

This book explores applications of computational intelligence in key and emerging fields of engineering, especially with regard to condition monitoring and fault diagnosis, inverse problems, decision support systems and optimization. These applications can be beneficial in a broad range of contexts, including: water distribution networks, manufacturing systems, production and storage of electrical energy, heat transfer, acoustic levitation, uncertainty and robustness of infinite-dimensional objects, fatigue failure prediction, autonomous navigation, nanotechnology, and the analysis of technological development indexes. All applications, mathematical and

The Science and Management of Uncertainty

The Science and Management of Uncertainty
  • Author : Bruce G. Marcot
  • Publisher : CRC Press
  • Release Date : 2020-11-26
  • ISBN : 9781000244519
GET THIS BOOKThe Science and Management of Uncertainty

Uncertainty can take many forms, can be represented in many ways, and can have important implications in decision-making and policy development. This book provides a rigorous scientific framework for dealing with uncertainty in real-world situations, and provides a comprehensive study of concepts, measurements, and applications of uncertainty in ecological modeling and natural resource management. The focus of this book is on the kinds and implications of uncertainty in environmental modeling and management, with practical guidelines and examples for successful modeling

Philosophies of Structural Safety and Reliability

Philosophies of Structural Safety and Reliability
  • Author : Vladimir Raizer,Isaac Elishakoff
  • Publisher : CRC Press
  • Release Date : 2022-07-28
  • ISBN : 9781000550740
GET THIS BOOKPhilosophies of Structural Safety and Reliability

Uncertainty is certain to be found in structural engineering, making it crucial to structure design. This book covers three competing philosophies behind structural safety and reliability: probabilistic analysis, fuzzy set-based treatments, and the convex approach. Explaining the theory behind probabilistic analysis, fuzzy set-based treatments, and the convex approach in detail, alongside their implementation, use, and benefits, the book compares and contrasts these methods, enabling the reader to solve problems associated with uncertainty. These uncertainty issues can be seen in civil

Bayesian Inference and Maximum Entropy Methods in Science and Engineering

Bayesian Inference and Maximum Entropy Methods in Science and Engineering
  • Author : Adriano Polpo,Julio Stern,Francisco Louzada,Rafael Izbicki,Hellinton Takada
  • Publisher : Springer
  • Release Date : 2018-07-12
  • ISBN : 9783319911434
GET THIS BOOKBayesian Inference and Maximum Entropy Methods in Science and Engineering

These proceedings from the 37th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2017), held in São Carlos, Brazil, aim to expand the available research on Bayesian methods and promote their application in the scientific community. They gather research from scholars in many different fields who use inductive statistics methods and focus on the foundations of the Bayesian paradigm, their comparison to objectivistic or frequentist statistics counterparts, and their appropriate applications. Interest in the

Variational Methods for Engineers with Matlab

Variational Methods for Engineers with Matlab
  • Author : Eduardo Souza de Cursi
  • Publisher : John Wiley & Sons
  • Release Date : 2015-10-02
  • ISBN : 9781119230151
GET THIS BOOKVariational Methods for Engineers with Matlab

This book is issued from a 30 years’ experience on the presentation of variational methods to successive generations of students and researchers in Engineering. It gives a comprehensive, pedagogical and engineer-oriented presentation of the foundations of variational methods and of their use in numerical problems of Engineering. Particular applications to linear and nonlinear systems of equations, differential equations, optimization and control are presented. MATLAB programs illustrate the implementation and make the book suitable as a textbook and for self-study. The evolution

Model Validation and Uncertainty Quantification, Volume 3

Model Validation and Uncertainty Quantification, Volume 3
  • Author : Robert Barthorpe,Roland Platz,Israel Lopez,Babak Moaveni,Costas Papadimitriou
  • Publisher : Springer
  • Release Date : 2017-06-07
  • ISBN : 9783319548586
GET THIS BOOKModel Validation and Uncertainty Quantification, Volume 3

Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 35th IMAC, A Conference and Exposition on Structural Dynamics, 2017, the third volume of ten from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Model Validation and Uncertainty Quantification, including papers on: Uncertainty Quantification in Material Models Uncertainty Propagation in Structural Dynamics Practical Applications of MVUQ Advances in Model Validation & Uncertainty Quantification: Model

Uncertainty Quantification

Uncertainty Quantification
  • Author : Christian Soize
  • Publisher : Springer
  • Release Date : 2017-04-24
  • ISBN : 9783319543390
GET THIS BOOKUncertainty Quantification

This book presents the fundamental notions and advanced mathematical tools in the stochastic modeling of uncertainties and their quantification for large-scale computational models in sciences and engineering. In particular, it focuses in parametric uncertainties, and non-parametric uncertainties with applications from the structural dynamics and vibroacoustics of complex mechanical systems, from micromechanics and multiscale mechanics of heterogeneous materials. Resulting from a course developed by the author, the book begins with a description of the fundamental mathematical tools of probability and statistics

An Introduction to Computational Stochastic PDEs

An Introduction to Computational Stochastic PDEs
  • Author : Gabriel J. Lord,Catherine E. Powell,Tony Shardlow
  • Publisher : Cambridge University Press
  • Release Date : 2014-08-11
  • ISBN : 9780521899901
GET THIS BOOKAn Introduction to Computational Stochastic PDEs

This book offers a practical presentation of stochastic partial differential equations arising in physical applications and their numerical approximation.

Topics in Model Validation and Uncertainty Quantification, Volume 5

Topics in Model Validation and Uncertainty Quantification, Volume 5
  • Author : Todd Simmermacher,Scott Cogan,Babak Moaveni,Costas Papadimitriou
  • Publisher : Springer Science & Business Media
  • Release Date : 2013-05-30
  • ISBN : 9781461465645
GET THIS BOOKTopics in Model Validation and Uncertainty Quantification, Volume 5

Topics in Model Validation and Uncertainty Quantification, Volume : Proceedings of the 31st IMAC, A Conference and Exposition on Structural Dynamics, 2013, the fifth volume of seven from the Conference, brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Structural Dynamics, including papers on: Uncertainty Quantification & Propagation in Structural Dynamics Robustness to Lack of Knowledge in Design Model Validation

Topics in Model Validation and Uncertainty Quantification, Volume 4

Topics in Model Validation and Uncertainty Quantification, Volume 4
  • Author : T. Simmermacher,Scott Cogan,L.G. Horta,R. Barthorpe
  • Publisher : Springer Science & Business Media
  • Release Date : 2012-04-23
  • ISBN : 9781461424314
GET THIS BOOKTopics in Model Validation and Uncertainty Quantification, Volume 4

Topics in Model Validation and Uncertainty Quantification, Volume 4, Proceedings of the 30th IMAC, A Conference and Exposition on Structural Dynamics, 2012, the fourth volume of six from the Conference, brings together 19 contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Structural Dynamics, including papers on: Robustness to Lack of Knowledge in Design Bayesian and Markov Chain Monte Carlo Methods Uncertainty Quantification Model Calibration

Numerical Methods for Stochastic Partial Differential Equations with White Noise

Numerical Methods for Stochastic Partial Differential Equations with White Noise
  • Author : Zhongqiang Zhang,George Em Karniadakis
  • Publisher : Springer
  • Release Date : 2017-09-01
  • ISBN : 9783319575117
GET THIS BOOKNumerical Methods for Stochastic Partial Differential Equations with White Noise

This book covers numerical methods for stochastic partial differential equations with white noise using the framework of Wong-Zakai approximation. The book begins with some motivational and background material in the introductory chapters and is divided into three parts. Part I covers numerical stochastic ordinary differential equations. Here the authors start with numerical methods for SDEs with delay using the Wong-Zakai approximation and finite difference in time. Part II covers temporal white noise. Here the authors consider SPDEs as PDEs driven