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008			160301s2016 gw a s 001 0 eng d
020			_a9783319299945
024	7		_a10.1007/978-3-319-29994-5 _2doi
040			_aES-MaUEC
050		4	_aQA402.2 _b.C84 2016 EB
082	0	4	_a620.1
100	1		_aCueto, Elías _0local _998764 _0Local
245	1	0	_aProper Generalized Decompositions : _bAn Introduction to Computer Implementation with Matlab _cby Elías Cueto, David González, Icíar Alfaro
250			_a1st ed.
264		1	_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XII, 96 p.) _b20 ilustraciones, 1 ilustraciones en color
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	1		_aSpringerBriefs in Applied Sciences and Technology _x2191-530X
505	0		_aIntroduction -- 2 To begin with: PGD for Poisson problems -- 2.1 Introduction -- 2.2 The Poisson problem -- 2.3 Matrix structure of the problem -- 2.4 Matlab code for the Poisson problem -- 3 Parametric problems -- 3.1 A particularly challenging problem: a moving load as a parameter -- 3.2 The problem under the PGD formalism -- 3.2.1 Computation of S(s) assuming R(x) is known -- 3.2.2 Computation of R(x) assuming S(s) is known -- 3.3 Matrix structure of the problem -- 3.4 Matlab code for the influence line problem -- 4 PGD for non-linear problems -- 4.1 Hyperelasticity -- 4.2 Matrix structure of the problem -- 4.2.1 Matrix form of the term T2 -- 4.2.2 Matrix form of the term T4 -- 4.2.3 Matrix form of the term T6 -- 4.2.4 Matrix form for the term T8 -- 4.2.5 Matrix form of the term T9 -- 4.2.6 Matrix form of the term T10 -- 4.2.7 Final comments -- 4.3 Matlab code -- 5 PGD for dynamical problems -- 5.1 Taking initial conditions as parameters -- 5.2 Developing the weak form of the problem -- 5.3 Matrix form of the problem -- 5.3.1 Time integration of the equations of motion -- 5.3.2 Computing a reduced-order basis for the field of initial conditions -- 5.3.3 Projection of the equations onto a reduced, parametric basis -- 5.4 Matlab code -- References -- Index.
520	3		_aThis book is intended to help researchers overcome the entrance barrier to Proper Generalized Decomposition (PGD), by providing a valuable tool to begin the programming task. Detailed Matlab Codes are included for every chapter in the book, in which the theory previously described is translated into practice. Examples include parametric problems, non-linear model order reduction and real-time simulation, among others. Proper Generalized Decomposition (PGD) is a method for numerical simulation in many fields of applied science and engineering. As a generalization of Proper Orthogonal Decomposition or Principal Component Analysis to an arbitrary number of dimensions, PGD is able to provide the analyst with very accurate solutions for problems defined in high dimensional spaces, parametric problems and even real-time simulation.
650		7	_aAnálisis de sistemas _0comprobar BNE19900959957 _2embne _9138446
650		7	_aFísica matemática _0comprobar BNE19900969835 _2embne _9139231
650		7	_aMatrices (Matemáticas) _0comprobar BNE19901022555 _2embne _9142711
650		7	_aModelos matemáticos _0comprobar BNE19900964919 _2embne _9405064
700	1		_aGonzález, David _0local _99061 _0comprobar BNE19941572320
700	1		_aAlfaro, Icíar _0local _927332 _0comprobar BNE20020438173
710	2		_aSpringerLink (Online service) _0Local _9106996
830		0	_aSpringerBriefs in Applied Sciences and Technology _x2191-530X _9134085
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-29994-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319299945
907			_a.b12949255 _b11-11-17 _c21-11-16
942			_2lcc _cLE
945			_aQA402.2 .C84 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11592515 _z06-04-17
988			_aEBOOK, EBSPRINGER
998			_am _a_alco _a_vill _b11-11-17 _cm _dz _ei _feng _ggw _h0