The Proper Generalized Decomposition for Advanced Numerical by Francisco Chinesta, Roland Keunings, Adrien Leygue

By Francisco Chinesta, Roland Keunings, Adrien Leygue

Many difficulties in medical computing are intractable with classical numerical innovations. those fail, for instance, within the resolution of high-dimensional versions as a result of the exponential bring up of the variety of levels of freedom.

Recently, the authors of this e-book and their collaborators have built a unique procedure, referred to as right Generalized Decomposition (PGD) that has confirmed to be an important leap forward. The PGD builds by way of a successive enrichment approach a numerical approximation of the unknown fields in a separated shape. even supposing first brought and effectively tested within the context of high-dimensional difficulties, the PGD allows a very new process for addressing extra general difficulties in technological know-how and engineering. certainly, many demanding difficulties may be successfully solid right into a multi-dimensional framework, therefore commencing solely new resolution concepts within the PGD framework. for example, the cloth parameters and boundary stipulations showing in a specific mathematical version should be considered as extra-coordinates of the matter as well as the standard coordinates similar to area and time. within the PGD framework, this enriched version is solved just once to yield a parametric answer that incorporates all specific ideas for particular values of the parameters.

The PGD has now attracted the eye of a big variety of learn teams all over the world. the current textual content is the 1st on hand ebook describing the PGD. It offers a truly readable and functional advent that permits the reader to speedy grab the most beneficial properties of the strategy. through the e-book, the PGD is utilized to difficulties of accelerating complexity, and the method is illustrated through conscientiously chosen numerical examples. in addition, the reader has loose entry to the Matlab© software program used to generate those examples.

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Extra resources for The Proper Generalized Decomposition for Advanced Numerical Simulations: A Primer

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28) i=1 p This again is an algebraic problem for the unknown function Yn (y). 3 The PGD Approximation Constructor in Action 53 There are two issues in the application of this procedure, which become important when the dimensionality of the problem increases. One lies in the calculation of β x (x) and β y (y). With the functions X i known at discrete positions xk , this implies the evaluation of a large number of integrals p−1 β x (xk ) = Yn (y) · f (xk , y) dy, k = 1, . . 29) y and similarly for β y (yl ), l = 1, .

5) . 59) The unknown functions X i (x) and Yi (y) are sought on a uniform grid with M = 41 points. All one-dimensional differential problems and integrals arising in the solution procedure are solved or computed using linear one-dimensional finite elements. 26), respectively. 60) where u FE,M (x, y) is the corresponding 2D finite element solution on an equivalent mesh. 4 Numerical Examples 43 Fig. 8 Normalized functions G x1 (x) and X i (x) for i = 1, . . , 4 Fig. 9 Normalized funcy tions G 1 (y) and Yi (y) for i = 1, .

4 Fig. 9 Normalized funcy tions G 1 (y) and Yi (y) for i = 1, . . , 4 y In Figs. 9, we show the normalized functions G x1 (x), X i (x), G 1 (y) and Yi (y) for i = 1, . . , 4. In Fig. 8 one can observe that only G x1 (x) is non-zero for x = 0 since all the X i (x) have to preserve the non-zero Dirichlet condition imposed y through G x1 (x) and G 1 (y). We first illustrate the pointwise convergence of the PGD solution towards the N for M = 41 and different reference solution in Fig. 10 where we plot u FE,M − u M values of N .

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