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008			181229s2019 gw a o |||| 0|eng d
020			_a9783030048709
024	7		_a10.1007/978-3-030-04870-9 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aTA340 _b2019 EB
245	0	0	_aUncertainty modeling for engineering applications _cedited by Flavio Canavero
264		1	_aCham _bSpringer International Publishing : _bImprint: Springer _c2019
300			_a1 recurso en línea (VIII, 184 páginas) _b97 ilustraciones, 88 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
490	0		_aPoliTO Springer Series _x2509-6796
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_aQuadrature 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	3		_aThis 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.
650		7	_2embne _aIngeniería _xMétodos estadísticos _9670301
650		7	_2embne _aFiabilidad (Ingeniería) _9141577
650		7	_2embne _aIncertidumbre (Teoría de la información) _9667868
700	1		_aCanavero, Flavio. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service) _9106996
776	0	8	_iPrinted edition: _z9783030048693
776	0	8	_iPrinted edition: _z9783030048716
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-04870-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aPrimersemestre_2019_Robotics
998			_aSI _cm _dz _feng _ggw _h0 _b10/2019 _eel _zSI