國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Introduction to uncertainty quantifi...

SpringerLink (Online service)

Introduction to uncertainty quantification

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Introduction to uncertainty quantificationby T.J. Sullivan.
作者:	Sullivan, T.J.
出版者:	Cham :Springer International Publishing :2015.
面頁冊數:	xii, 342 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	UncertaintyMathematical models.
電子資源:	http://dx.doi.org/10.1007/978-3-319-23395-6
ISBN:	9783319233956$q(electronic bk.)

Introduction to uncertainty quantification
Sullivan, T.J.

Introduction to uncertainty quantification[electronic resource] /by T.J. Sullivan. - Cham :Springer International Publishing :2015. - xii, 342 p. :ill., digital ;24 cm. - Texts in applied mathematics,v.630939-2475 ;. - Texts in applied mathematics ;40..

Introduction -- 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.

Uncertainty 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.

ISBN: 9783319233956$q(electronic bk.)

Standard No.: 10.1007/978-3-319-23395-6doiSubjects--Topical Terms:

263216
Uncertainty
--Mathematical models.

LC Class. No.: QA273

Dewey Class. No.: 519.2

Introduction to uncertainty quantification
LDR:02902nmm a2200337 a 4500 001 477716
003 DE-He213
005 20160512151346.0
006 m d
007 cr nn 008maaau
008 160614s2015 gw s 0 eng d
020 $a 9783319233956$q(electronic bk.)
020 $a 9783319233949$q(paper)
024 7 $a 10.1007/978-3-319-23395-6 $2 doi
035 $a 978-3-319-23395-6
040 $a GP $c GP
041 0 $a eng
050 4 $a QA273
072 7 $a PBT $2 bicssc
072 7 $a PBWL $2 bicssc
072 7 $a MAT029000 $2 bisacsh
082 0 4 $a 519.2 $2 23
090 $a QA273 $b .S952 2015
100 1 $a Sullivan, T.J. $3 732727
245 1 0 $a Introduction to uncertainty quantification $h [electronic resource] / $c by T.J. Sullivan.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a xii, 342 p. : $b ill., digital ; $c 24 cm.
490 1 $a Texts in applied mathematics, $x 0939-2475 ; $v v.63
505 0 $a Introduction -- 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 $a Uncertainty 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 $a Uncertainty $x Mathematical models. $3 263216
650 1 4 $a Mathematics. $3 184409
650 2 4 $a Probability Theory and Stochastic Processes. $3 274061
650 2 4 $a Optimization. $3 274084
650 2 4 $a Numerical Analysis. $3 275681
650 2 4 $a Appl.Mathematics/Computational Methods of Engineering. $3 273758
650 2 4 $a Theoretical, Mathematical and Computational Physics. $3 376743
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Texts in applied mathematics ; $v 40. $3 558788
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-23395-6
950 $a Mathematics and Statistics (Springer-11649)