000			a
999			_c29797 _d29797
008			191120b xxu||||| |||| 00| 0 eng d
020			_a9783319543383
082			_a620.0015192 _bSOI
100			_aSoize, Christian
245			_aUncertainty quantification : an accelerated course with advanced applications in computational engineering
260			_aCham _bSpringer _c2017
300			_axxii, 329 p. _bill. (some col.) _c24 cm
365			_b74.99 _cEUR _d82.00
490			_aInterdisciplinary applied mathematics _vv. 47
504			_aIncludes bibliographical references and index.
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			_aInformation theory
650			_aStatistical methods
650			_aData processing
650			_aStochastic models
650			_aApplied Mathematics
650			_aComputational Methods of Engineering
650			_aEngineering mathematics
650			_aProbability Theory and Stochastic Processes
942			_2ddc _cBK