000			03847nam a22003615i 4500
001			103953
003			DE-He213
005			20230102113153.0
007			cr nn 008mamaa
008			150310s2015 gw | s |||| 0|eng d
020			_a9783319107141
024	7		_a10.1007/978-3-319-10714-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
050		4	_aQA377 2015 EB
100	1		_aPettersson, Mass Per _eautor _9669718
245	1	0	_aPolynomial Chaos Methods for Hyperbolic Partial Differential Equations _bNumerical Techniques for Fluid Dynamics Problems in the Presence of Uncertainties _cby Mass Per Pettersson, Gianluca Iaccarino, Jan Nordström.
264		1	_aCham _bSpringer International Publishing _c2015
300			_a1 recurso en línea (XI, 214 páginas 60 ilustraciones, 54 ilustraciones a color.)
490	0		_aMathematical Engineering _x2192-4732
505	0		_aRandom Field Representation -- Polynomial Chaos Methods -- Numerical Solution of Hyperbolic Problems -- Linear Transport -- Nonlinear Transport -- Boundary Conditions and Data -- Euler Equations -- A Hybrid Scheme for Two-Phase Flow -- Appendices.
520	3		_aThis monograph presents computational techniques and numerical analysis to study conservation laws under uncertainty using the stochastic Galerkin formulation. With the continual growth of computer power, these methods are becoming increasingly popular as an alternative to more classical sampling-based techniques. The approach described in the text takes advantage of stochastic Galerkin projections applied to the original conservation laws to produce a large system of modified partial differential equations, the solutions to which directly provide a full statistical characterization of the effect of uncertainties. Polynomial Chaos Methods of Hyperbolic Partial Differential Equations focuses on the analysis of stochastic Galerkin systems obtained for linear and non-linear convection-diffusion equations and for a systems of conservation laws; a detailed well-posedness and accuracy analysis is presented to enable the design of robust and stable numerical methods. The exposition is restricted to one spatial dimension and one uncertain parameter as its extension is conceptually straightforward. The numerical methods designed guarantee that the solutions to the uncertainty quantification systems will converge as the mesh size goes to zero. Examples from computational fluid dynamics are presented together with numerical methods suitable for the problem at hand: stable high-order finite-difference methods based on summation-by-parts operators for smooth problems, and robust shock-capturing methods for highly nonlinear problems. Academics and graduate students interested in computational fluid dynamics and uncertainty quantification will find this book of interest. Readers are expected to be familiar with the fundamentals of numerical analysis. Some background in stochastic methods is useful but not necessary.
650		7	_aEcuaciones diferenciales _2embne _9139227
650		7	_aAnálisis numérico _2embne _9405025
700	1		_aIaccarino, Gianluca _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/no2010154665 _1http://viaf.org/viaf/206230342/
700	1		_aNordström, Jan _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _1http://viaf.org/viaf/50241319/
776	0	8	_iEdición impresa: _z9783319107158
776	0	8	_iEdición impresa: _z9783319107134
776	0	8	_iEdición impresa: _z9783319356129
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-10714-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
490	0		_aEngineering (Springer-11647)
988			_aEBSPRINGER_2018
998			_b06/2019 _dz _ek _feng _ggw _h0
999			_c103953 _d103953 _x1