Download Certified Reduced Basis Methods for Parametrized Partial by Jan S Hesthaven, Gianluigi Rozza, Benjamin Stamm PDF

By Jan S Hesthaven, Gianluigi Rozza, Benjamin Stamm

This ebook offers an intensive creation to the mathematical and algorithmic points of qualified diminished foundation equipment for parametrized partial differential equations. valuable elements starting from version building, mistakes estimation and computational potency to empirical interpolation equipment are mentioned intimately for coercive difficulties. extra complicated facets linked to time-dependent difficulties, non-compliant and non-coercive difficulties and purposes with geometric version also are mentioned as examples.

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Example text

But in all cases, η(μ) consist of a strict upper bound of the corresponding error-quantity. As already mentioned, details are postponed to Chap. 4. During this iterative basis selection process and if at the n-th step a n-dimensional reduced basis space Vrb is given, the next basis function is the one that maximizes the estimated model order reduction error given the n-dimensional space Vrb over P. 10) and compute u δ (μn+1 ) to enrich the reduced basis space as Vrb = span{u δ }(μ1 ), . . , u δ (μn+1 ).

It can consist of a regular lattice or a randomly generated point-set intersecting with P. We can then introduce the following set Mδ (Ph ) = {u δ (μ) | μ ∈ Ph } of cardinality M = |Ph |. Of course, it holds that Mδ (Ph ) ⊂ Mδ as Ph ⊂ P but if Ph is fine enough, Mδ (Ph ) is also a good representation of Mδ . 1 Proper Orthogonal Decomposition (POD) Proper Orthogonal Decomposition (POD) is an explore-and-compress strategy in which one samples the parameter space, compute the corresponding truth solutions at all sample points and, following compression, retains only the essential information.

Since this evaluation is needed during the online stage of the reduced basis method to certify the output, the computation of rˆδ (μ) V must be efficient and at a cost independent of Nδ (the dimension of Vδ ). 2 Error Control for the Reduced Order Model 53 μ N recalling that u rb (μ) = n=1 (urb )n ξn . Let us next introduce the coefficient vector Q r(μ) ∈ R r , with Q r = Q f + Q a N terms, as μ Q μ r(μ) = θf1 (μ), . . , θf f (μ), −(urb )T θa1 (μ), . . , −(urb )T θaQ a (μ) T . With a similar ordering, we define the vectors of forms F ∈ (Vδ ) Q f and Aq ∈ (Vδ ) N for 1 ≤ q ≤ Q a as F = f1 , .

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