Local proper generalized decomposition |
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Authors: | Alberto Badías David González Iciar Alfaro Francisco Chinesta Elias Cueto |
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Affiliation: | 1. Aragon Institute of Engineering Research, Universidad de Zaragoza, Zaragoza, Spain;2. Institute of High‐Performance Computing, ICI, Ecole Centrale de Nantes, France |
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Abstract: | One of the main difficulties that a reduced‐order method could face is the poor separability of the solution. This problem is common to both a posteriori model order reduction (proper orthogonal decomposition, reduced basis) and a priori proper generalized decomposition (PGD)] model order reduction. Early approaches to solve it include the construction of local reduced‐order models in the framework of POD. We present here an extension of local models in a PGD—and thus, a priori—context. Three different strategies are introduced to estimate the size of the different patches or regions in the solution manifold where PGD is applied. As will be noticed, no gluing or special technique is needed to deal with the resulting set of local reduced‐order models, in contrast to most proper orthogonal decomposition local approximations. The resulting method can be seen as a sort of a priori manifold learning or nonlinear dimensionality reduction technique. Examples are shown that demonstrate pros and cons of each strategy for different problems. |
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Keywords: | kernel principal component analysis local model order reduction nonlinear dimensionality reduction proper generalized decomposition |
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