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Randomized linear algebra for model order reduction

Author
Balabanov, O.
Type of activity
Theses
Defense's date
2019-10-11
Repository
https://tel.archives-ouvertes.fr/tel-02444461 Open in new window
Abstract
Solutions to high-dimensional parameter-dependent problems are in great demand in the contemporary applied science and engineering. The standard approximation methods for parametric equations can require computational resources that are exponential in the dimension of the parameter space, which is typically referred to as the curse of dimensionality. To break the curse of dimensionality one has to appeal to nonlinear methods that exploit the structure of the solution map, such as projection-base...
Keywords
Distcionary-based approximation, Model order reduction, Parameter-dependent equations, Preconditioner, Random sketching, Reduced basis, Subspace embedding
Group of research
LACÀN - Numerical Methods for Applied Sciences and Engineering

Participants