Titlebook: Reduced Order Methods for Modeling and Computational Reduction; Alfio Quarteroni,Gianluigi Rozza Book 2014 Springer International Publishi

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Alfio Quarteroni,Gianluigi RozzaA complete review on the state of the art of model order reduction advances and developments.A gallery of application examples on reduced order modeling in computational science and engineering.It cov

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https://doi.org/10.1007/978-3-319-02090-7computational mechanics; model order reduction; parametrized PDE; reduced order modeling; scientific com

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Case Study: Parametrized Reduction Using Reduced-Basis and the Loewner Framework,e having the same goal of constructing reduced-order models for large-scale parameter-dependent systems, the two methods follow fundamentally different approaches. On the one hand, the well known Reduced-Basis method takes a time domain approach, using offline snapshots of the full-order system comb

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Application of the Discrete Empirical Interpolation Method to Reduced Order Modeling of Nonlinear arametrically varying problems the cost of evaluating these ROMs still depends on the size of the full order model and therefore is still expensive. The Discrete Empirical Interpolation Method (DEIM) further approximates the nonlinearity in the projection based ROM. The resulting DEIM ROM nonlinearit

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A Robust Algorithm for Parametric Model Order Reduction Based on Implicit Moment Matching,n system and control theory as well as computational electromagnetics and nanoelectronics, are methods based on multi-moment matching. Despite numerous other successful methods, including the reduced-basis method (RBM), other methods based on (rational, matrix, manifold) interpolation, or Kriging te

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