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Solveurs performants pour l'optimisation sous contraintes en identification de paramètres

Abstract : This thesis aims at designing efficient numerical solution methods to solve linear systems, arising in constrained optimization problems in some structural dynamics and vibration applications (test-analysis correlation, model error localization,hybrid model, damage assessment, etc.). These applications rely on solving inverse problems, by means of minimization of an energy-based functional. This latter involves both data from a numerical finite element model and from experimental tests, which leads to high quality models, but the associated linear systems, that have a saddle-point coefficient matrices, are long and costly to solve. We propose two different classes of methods to deal with these problems. First, a direct factorization method that takes advantage of the special structures and properties of these saddle point matrices. The Gaussian elimination factorization is implemented in order to factorize the saddle point matrices block-wise with small blocks of orders 2 and using a fill-in reducing topological ordering. We obtain significant gains in memory cost (up to 50%) due to enhanced factors sparsity in comparison to literature. The second class is based on a double projection of the generated saddle point system onto the nullspace of the constraints. The first projection onto the kinematic constraints is proposed as an explicit process through the computation of a sparse null basis. Then, we detail the application of a constraint preconditioner within a Krylov subspace solver, as an implicit second projection of the system onto the nullspace of the sensors constraints. We further present and compare different approximations of the constraint preconditioner. The approach is implemented in a parallel distributed environment using the PETSc library. Significant gains in computational cost and memory are illustrated on several industrial applications.
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Submitted on : Tuesday, December 12, 2017 - 12:20:50 AM
Last modification on : Wednesday, July 8, 2020 - 11:10:21 AM


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  • HAL Id : tel-01661459, version 1


Naoufal Nifa. Solveurs performants pour l'optimisation sous contraintes en identification de paramètres. Autre. Université Paris-Saclay, 2017. Français. ⟨NNT : 2017SACLC066⟩. ⟨tel-01661459⟩



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