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Uncertainty modeling for engineering...

Canavero, Flavio.

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Uncertainty modeling for engineering applications

Record Type:	Electronic resources : Monograph/item
Title/Author:	Uncertainty modeling for engineering applications/ edited by Flavio Canavero.
other author:	Canavero, Flavio.
Published:	Cham :Springer International Publishing : : 2019.,
Description:	viii, 184 p. :ill., digital ;24 cm.
[NT 15003449]:	Quadrature Strategies for Constructing Polynomial Approximations -- Weighted reduced order methods for parametrized partial differential equations with random inputs -- A new approach for state estimation -- Data-efficient Sensitivity Analysis with Surrogate Modeling -- Application of Polynomial Chaos Expansions for Uncertainty Estimation in Angle-of-Arrival based Localization -- Surrogate Modeling for Fast Experimental Assessment of Specific Absorption Rate -- Stochastic Dosimetry for Radio-Frequency exposure assessment in realistic scenarios -- On the Various Applications of Stochastic Collocation in Computational Electromagnetics -- Reducing the statistical complexity of EMC testing: improvements for radiated experiments using stochastic collocation and bootstrap methods -- Hybrid Possibilistic-Probabilistic Approach to Uncertainty Quantification in Electromagnetic Compatibility Models -- Measurement uncertainty cannot always be calculated.
Contained By:	Springer eBooks
Subject:	Engineering - Statistical methods. -
Online resource:	https://doi.org/10.1007/978-3-030-04870-9
ISBN:	9783030048709

Uncertainty modeling for engineering applications
Uncertainty modeling for engineering applications[electronic resource] /edited by Flavio Canavero. - Cham :Springer International Publishing :2019. - viii, 184 p. :ill., digital ;24 cm. - PoliTO Springer series,2509-6796. - PoliTO Springer series..

Quadrature Strategies for Constructing Polynomial Approximations -- Weighted reduced order methods for parametrized partial differential equations with random inputs -- A new approach for state estimation -- Data-efficient Sensitivity Analysis with Surrogate Modeling -- Application of Polynomial Chaos Expansions for Uncertainty Estimation in Angle-of-Arrival based Localization -- Surrogate Modeling for Fast Experimental Assessment of Specific Absorption Rate -- Stochastic Dosimetry for Radio-Frequency exposure assessment in realistic scenarios -- On the Various Applications of Stochastic Collocation in Computational Electromagnetics -- Reducing the statistical complexity of EMC testing: improvements for radiated experiments using stochastic collocation and bootstrap methods -- Hybrid Possibilistic-Probabilistic Approach to Uncertainty Quantification in Electromagnetic Compatibility Models -- Measurement uncertainty cannot always be calculated.

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. The collection in one place of different methodologies for different applications has the great value of stimulating the cross-fertilization and alleviate the language barrier among areas sharing a common background of mathematical modeling for problem solution. The book is designed for researchers, professionals and graduate students interested in quantitatively assessing the effects of uncertainties in their fields of application. The contents build upon the workshop "Uncertainty Modeling for Engineering Applications" (UMEMA 2017), held in Torino, Italy in November 2017.

ISBN: 9783030048709

Standard No.: 10.1007/978-3-030-04870-9doiSubjects--Topical Terms:

646559
Engineering
--Statistical methods.

LC Class. No.: TA340 / .U53 2019

Dewey Class. No.: 519.2

Uncertainty modeling for engineering applications
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245 0 0 $a Uncertainty modeling for engineering applications $h [electronic resource] / $c edited by Flavio Canavero.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a viii, 184 p. : $b ill., digital ; $c 24 cm.
490 1 $a PoliTO Springer series, $x 2509-6796
505 0 $a Quadrature Strategies for Constructing Polynomial Approximations -- Weighted reduced order methods for parametrized partial differential equations with random inputs -- A new approach for state estimation -- Data-efficient Sensitivity Analysis with Surrogate Modeling -- Application of Polynomial Chaos Expansions for Uncertainty Estimation in Angle-of-Arrival based Localization -- Surrogate Modeling for Fast Experimental Assessment of Specific Absorption Rate -- Stochastic Dosimetry for Radio-Frequency exposure assessment in realistic scenarios -- On the Various Applications of Stochastic Collocation in Computational Electromagnetics -- Reducing the statistical complexity of EMC testing: improvements for radiated experiments using stochastic collocation and bootstrap methods -- Hybrid Possibilistic-Probabilistic Approach to Uncertainty Quantification in Electromagnetic Compatibility Models -- Measurement uncertainty cannot always be calculated.
520 $a 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. The collection in one place of different methodologies for different applications has the great value of stimulating the cross-fertilization and alleviate the language barrier among areas sharing a common background of mathematical modeling for problem solution. The book is designed for researchers, professionals and graduate students interested in quantitatively assessing the effects of uncertainties in their fields of application. The contents build upon the workshop "Uncertainty Modeling for Engineering Applications" (UMEMA 2017), held in Torino, Italy in November 2017.
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650 0 $a Reliability (Engineering) $x Statistical methods. $3 646295
650 0 $a Risk assessment $x Statistical methods. $3 750248
650 0 $a Uncertainty (Information theory) $3 587701
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Quality Control, Reliability, Safety and Risk. $3 891027
650 2 4 $a Probability Theory and Stochastic Processes. $3 891080
700 1 $a Canavero, Flavio. $3 3383402
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830 0 $a PoliTO Springer series. $3 3206204
856 4 0 $u https://doi.org/10.1007/978-3-030-04870-9
950 $a Intelligent Technologies and Robotics (Springer-42732)