Model reduction by proper orthogonal decomposition for estimation of scalar parameters in elliptic PDEs
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Abstract
Proper orthogonal decomposition (POD) is a powerful technique for model reduction of linear and non-linear systems. It is based on a Galerkin type discretization with basis elements created from the system itself. In this work POD is applied to estimate scalar parameters in elliptic partial differential equations. The parameter estimation is formulated in terms of an optimal control problem that is solved by an augmented Lagrangian method combined with a sequential quadratic programming algorithm. Numerical examples illustrate the efficiency of the proposed approach.
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