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Uncertainty quantification = an acce...

Soize, Christian.

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Uncertainty quantification = an accelerated course with advanced applications in computational engineering /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Uncertainty quantification/ by Christian Soize.
其他題名:	an accelerated course with advanced applications in computational engineering /
作者:	Soize, Christian.
出版者:	Cham :Springer International Publishing : : 2017.,
面頁冊數:	xxii, 329 p. :ill. (some col.), digital ;24 cm.
內容註:	Fundamental Notions in Stochastic Modeling of Uncertainties and their Propagation in Computational Models -- Elements of Probability Theory -- Markov Process and Stochastic Differential Equation -- MCMC Methods for Generating Realizations and for Estimating the Mathematical Expectation of Nonlinear Mappings of Random Vectors -- Fundamental Probabilistic Tools for Stochastic Modeling of Uncertainties -- Brief Overview of Stochastic Solvers for the Propagation of Uncertainties -- Fundamental Tools for Statistical Inverse Problems -- Uncertainty Quantification in Computational Structural Dynamics and Vibroacoustics -- Robust Analysis with Respect to the Uncertainties for Analysis, Updating, Optimization, and Design -- Random Fields and Uncertainty Quantification in Solid Mechanics of Continuum Media.
Contained By:	Springer eBooks
標題:	Uncertainty - Mathematical models. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-54339-0
ISBN:	9783319543390

Uncertainty quantification = an accelerated course with advanced applications in computational engineering /
Soize, Christian.

Uncertainty quantificationan accelerated course with advanced applications in computational engineering /[electronic resource] :by Christian Soize. - Cham :Springer International Publishing :2017. - xxii, 329 p. :ill. (some col.), digital ;24 cm. - Interdisciplinary applied mathematics,v.470939-6047 ;. - Interdisciplinary applied mathematics ;v.47..

Fundamental Notions in Stochastic Modeling of Uncertainties and their Propagation in Computational Models -- Elements of Probability Theory -- Markov Process and Stochastic Differential Equation -- MCMC Methods for Generating Realizations and for Estimating the Mathematical Expectation of Nonlinear Mappings of Random Vectors -- Fundamental Probabilistic Tools for Stochastic Modeling of Uncertainties -- Brief Overview of Stochastic Solvers for the Propagation of Uncertainties -- Fundamental Tools for Statistical Inverse Problems -- Uncertainty Quantification in Computational Structural Dynamics and Vibroacoustics -- Robust Analysis with Respect to the Uncertainties for Analysis, Updating, Optimization, and Design -- Random Fields and Uncertainty Quantification in Solid Mechanics of Continuum Media.

ISBN: 9783319543390

Standard No.: 10.1007/978-3-319-54339-0doiSubjects--Topical Terms:

648813
Uncertainty
--Mathematical models.

LC Class. No.: QA273

Dewey Class. No.: 003.54

Uncertainty quantification = an accelerated course with advanced applications in computational engineering /
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