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001 | 978-3-319-54339-0 | ||
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_a9783319543390 _9978-3-319-54339-0 |
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_a10.1007/978-3-319-54339-0 _2doi |
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_a004 _223 |
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_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. |
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300 |
_aXXII, 329 p. 110 illus., 86 illus. in color. _bonline resource. |
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336 |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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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 |