A data compression technique for PGD reduced-order modeling

Abstract : This work concerns the Proper Generalized Decomposition (PGD), an a priori model reduction technique used to solve problems, eventually nonlinear, defined over the time-space domain. PGD seeks the solution of a problem in a reduced-order basis generated by a dedicated algorithm. This is the LATIN method, an iterative strategy which generates the approximations of the solution over the entire time-space domain by successive enrichments. Herein an algebraic framework adapted to PGD is proposed. It defines a compressed version of the data making less expensive the elementary algebraic operations.
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Submitted on : Friday, August 30, 2013 - 10:53:02 AM
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Matteo Capaldo, David Néron, Pierre Ladevèze, Pierre-Alain Guidault. A data compression technique for PGD reduced-order modeling. 2nd ECCOMAS Young Investigators Conference (YIC 2013), Sep 2013, Bordeaux, France. ⟨hal-00855902⟩



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