000 03984nam a22005775i 4500
001 vtls000560740
003 RU-ToGU
005 20210922090203.0
007 cr nn 008mamaa
008 170212s2015 gw | s |||| 0|eng d
020 _a9783319233956
_9978-3-319-23395-6
024 7 _a10.1007/978-3-319-23395-6
_2doi
035 _ato000560740
040 _aSpringer
_cSpringer
_dRU-ToGU
050 4 _aQA273.A1-274.9
050 4 _aQA274-274.9
072 7 _aPBT
_2bicssc
072 7 _aPBWL
_2bicssc
072 7 _aMAT029000
_2bisacsh
082 0 4 _a519.2
_223
100 1 _aSullivan, T.J.
_eauthor.
_9467321
245 1 0 _aIntroduction to Uncertainty Quantification
_helectronic resource
_cby T.J. Sullivan.
260 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _aXII, 342 p. 28 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 1 _aTexts in Applied Mathematics,
_x0939-2475 ;
_v63
505 0 _aIntroduction -- Measure and Probability Theory -- Banach and Hilbert Spaces -- Optimization Theory -- Measures of Information and Uncertainty -- Bayesian Inverse Problems -- Filtering and Data Assimilation -- Orthogonal Polynomials and Applications -- Numerical Integration -- Sensitivity Analysis and Model Reduction -- Spectral Expansions -- Stochastic Galerkin Methods -- Non-Intrusive Methods -- Distributional Uncertainty -- References -- Index.
520 _aUncertainty quantification is a topic of increasing practical importance at the intersection of applied mathematics, statistics, computation, and numerous application areas in science and engineering. This text provides a framework in which the main objectives of the field of uncertainty quantification are defined, and an overview of the range of mathematical methods by which they can be achieved. Complete with exercises throughout, the book will equip readers with both theoretical understanding and practical experience of the key mathematical and algorithmic tools underlying the treatment of uncertainty in modern applied mathematics. Students and readers alike are encouraged to apply the mathematical methods discussed in this book to their own favourite problems to understand their strengths and weaknesses, also making the text suitable as a self-study. This text is designed as an introduction to uncertainty quantification for senior undergraduate and graduate students with a mathematical or statistical background, and also for researchers from the mathematical sciences or from applications areas who are interested in the field. T. J. Sullivan was Warwick Zeeman Lecturer at the Mathematics Institute of the University of Warwick, United Kingdom, from 2012 to 2015.  Since 2015, he is Junior Professor of Applied Mathematics at the Free University of Berlin, Germany, with specialism in Uncertainty and Risk Quantification.
650 0 _amathematics.
_9566183
650 0 _aNumerical analysis.
_9566288
650 0 _aMathematical optimization.
_9566241
650 0 _aProbabilities.
_9295556
650 0 _aphysics.
_9566227
650 0 _aApplied mathematics.
_9460111
650 0 _aEngineering mathematics.
_9303575
650 1 4 _aMathematics.
_9566184
650 2 4 _aProbability Theory and Stochastic Processes.
_9303734
650 2 4 _aoptimization.
_9566242
650 2 4 _aNumerical Analysis.
_9566289
650 2 4 _aAppl.Mathematics/Computational Methods of Engineering.
_9303577
650 2 4 _aTheoretical, Mathematical and Computational Physics.
_9410498
710 2 _aSpringerLink (Online service)
_9143950
773 0 _tSpringer eBooks
830 0 _aTexts in Applied Mathematics,
_9298055
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-23395-6
912 _aZDB-2-SMA
999 _c415074