A data compression technique for PGD reduced-order modeling

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.