000 04125nam a22005175i 4500
001 978-3-319-54339-0
003 DE-He213
005 20181204134421.0
007 cr nn 008mamaa
008 170424s2017 gw | s |||| 0|eng d
020 _a9783319543390
_9978-3-319-54339-0
024 7 _a10.1007/978-3-319-54339-0
_2doi
040 _aISI Library, Kolkata
050 4 _aQA71-90
072 7 _aPDE
_2bicssc
072 7 _aCOM014000
_2bisacsh
072 7 _aPDE
_2thema
082 0 4 _a004
_223
100 1 _aSoize, Christian.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aUncertainty Quantification
_h[electronic resource] :
_bAn Accelerated Course with Advanced Applications in Computational Engineering /
_cby Christian Soize.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXXII, 329 p. 110 illus., 86 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aInterdisciplinary Applied Mathematics,
_x0939-6047 ;
_v47
505 0 _aFundamental 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.
520 _aThis book presents the fundamental notions and advanced mathematical tools in the stochastic modeling of uncertainties and their quantification for large-scale computational models in sciences and engineering. In particular, it focuses in parametric uncertainties, and non-parametric uncertainties with applications from the structural dynamics and vibroacoustics of complex mechanical systems, from micromechanics and multiscale mechanics of heterogeneous materials. Resulting from a course developed by the author, the book begins with a description of the fundamental mathematical tools of probability and statistics that are directly useful for uncertainty quantification. It proceeds with a well carried out description of some basic and advanced methods for constructing stochastic models of uncertainties, paying particular attention to the problem of calibrating and identifying a stochastic model of uncertainty when experimental data is available. < This book is intended to be a graduate-level textbook for students as well as professionals interested in the theory, computation, and applications of risk and prediction in science and engineering fields.
650 0 _aComputer science.
650 0 _aEngineering mathematics.
650 0 _aDistribution (Probability theory.
650 1 4 _aComputational Science and Engineering.
_0http://scigraph.springernature.com/things/product-market-codes/M14026
650 2 4 _aMathematical and Computational Engineering.
_0http://scigraph.springernature.com/things/product-market-codes/T11006
650 2 4 _aProbability Theory and Stochastic Processes.
_0http://scigraph.springernature.com/things/product-market-codes/M27004
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319543383
776 0 8 _iPrinted edition:
_z9783319543406
776 0 8 _iPrinted edition:
_z9783319853727
830 0 _aInterdisciplinary Applied Mathematics,
_x0939-6047 ;
_v47
856 4 0 _uhttps://doi.org/10.1007/978-3-319-54339-0
912 _aZDB-2-SMA
942 _cEB
950 _aMathematics and Statistics (Springer-11649)
999 _c427260
_d427260