The proper orthogonal decomposition method and Arnoldi-based Krylov projection method are investigated in order to reduce a finite-element model of a quasi-static problem. Both methods are compared on an academic example in terms of computation time and precision
In dieser Arbeit werden effiziente Verfahren zur Finite-Elemente-Simulation parametrischer Wirbelstr...
Our world today is becoming increasingly complex, and technical devices are getting ever smaller and...
The Proper Orthogonal Decomposition (POD) combined with the (Discrete) Empirical Interpolation Metho...
The proper orthogonal decomposition method and Arnoldi-based Krylov projection method are investigat...
International audienceThe Proper Orthogonal Decomposition method and the Arnoldi-based Krylov projec...
In this paper, the proper orthogonal decomposition and the Arnoldi-based Krylov subspace methods are...
Among the model order reduction techniques, the Proper Orthogonal Decomposition (POD) has shown its ...
In the domain of numerical computation, Model Order Reduction approaches are more and more frequentl...
In order to reduce the computation time and the memory resources required to solve an electromagneti...
We consider model reduction of Maxwell's equations arising in magneto-quasistatic field problems. A ...
In this paper, a proper-orthogonal-decomposition reduced-order model is applied to an eddy-current p...
Model Order Reduction (MOR) methods are applied in different areas of physics in order to reduce the...
Proper Orthogonal Decomposition (POD) has been successfully used to reduce the size of linear Finite...
The Proper Orthogonal Decomposition (POD) combined with the (Discrete) Empirical Interpolation Metho...
In this paper, reduced order modeling (ROM) based on the proper orthogonal decomposition (POD) are a...
In dieser Arbeit werden effiziente Verfahren zur Finite-Elemente-Simulation parametrischer Wirbelstr...
Our world today is becoming increasingly complex, and technical devices are getting ever smaller and...
The Proper Orthogonal Decomposition (POD) combined with the (Discrete) Empirical Interpolation Metho...
The proper orthogonal decomposition method and Arnoldi-based Krylov projection method are investigat...
International audienceThe Proper Orthogonal Decomposition method and the Arnoldi-based Krylov projec...
In this paper, the proper orthogonal decomposition and the Arnoldi-based Krylov subspace methods are...
Among the model order reduction techniques, the Proper Orthogonal Decomposition (POD) has shown its ...
In the domain of numerical computation, Model Order Reduction approaches are more and more frequentl...
In order to reduce the computation time and the memory resources required to solve an electromagneti...
We consider model reduction of Maxwell's equations arising in magneto-quasistatic field problems. A ...
In this paper, a proper-orthogonal-decomposition reduced-order model is applied to an eddy-current p...
Model Order Reduction (MOR) methods are applied in different areas of physics in order to reduce the...
Proper Orthogonal Decomposition (POD) has been successfully used to reduce the size of linear Finite...
The Proper Orthogonal Decomposition (POD) combined with the (Discrete) Empirical Interpolation Metho...
In this paper, reduced order modeling (ROM) based on the proper orthogonal decomposition (POD) are a...
In dieser Arbeit werden effiziente Verfahren zur Finite-Elemente-Simulation parametrischer Wirbelstr...
Our world today is becoming increasingly complex, and technical devices are getting ever smaller and...
The Proper Orthogonal Decomposition (POD) combined with the (Discrete) Empirical Interpolation Metho...