Speeding up in SSFEM computation using ...
Document type :
Communication dans un congrès avec actes
Permalink :
Title :
Speeding up in SSFEM computation using Kronecker tensor products
Author(s) :
Gaignaire, Roman [Auteur]
Laboratoire d’Électrotechnique et d’Électronique de Puissance - ULR 2697 [L2EP]
Guyomarch, Frédéric [Auteur]
Contributions of the Data parallelism to real time [DART]
Moreau, O [Auteur]
EDF [EDF]
Clenet, Stephane [Auteur]
Laboratoire d'Électrotechnique et d'Électronique de Puissance (L2EP) - ULR 2697
Sudret, B [Auteur]
EDF [EDF]
Laboratoire d’Électrotechnique et d’Électronique de Puissance - ULR 2697 [L2EP]
Guyomarch, Frédéric [Auteur]

Contributions of the Data parallelism to real time [DART]
Moreau, O [Auteur]
EDF [EDF]
Clenet, Stephane [Auteur]

Laboratoire d'Électrotechnique et d'Électronique de Puissance (L2EP) - ULR 2697
Sudret, B [Auteur]
EDF [EDF]
Conference title :
13th Biennial IEEE Conference on Electromagnetic Field Computation (CEFC)
City :
Athens
Country :
Grèce
Start date of the conference :
2008-05-11
English keyword(s) :
Finite Element Method
Electrokinetic's
Hermite polynomial chaos
Linear system resolution
Random media
Electrokinetic's
Hermite polynomial chaos
Linear system resolution
Random media
HAL domain(s) :
Sciences de l'ingénieur [physics]/Electromagnétisme
English abstract : [en]
The spectral stochastic finite-element method makes it possible to convey some random aspects of input data to the output data. However, the system size dramatically increases with the number of input random variables. ...
Show more >The spectral stochastic finite-element method makes it possible to convey some random aspects of input data to the output data. However, the system size dramatically increases with the number of input random variables. Using matrix Kronecker tensor products for system solving noticeably reduces the computation time and the storage requirements.Show less >
Show more >The spectral stochastic finite-element method makes it possible to convey some random aspects of input data to the output data. However, the system size dramatically increases with the number of input random variables. Using matrix Kronecker tensor products for system solving noticeably reduces the computation time and the storage requirements.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Research team(s) :
Équipe Outils et Méthodes Numériques
Submission date :
2020-05-15T13:43:21Z
2022-03-08T18:03:06Z
2022-03-08T18:03:06Z
Files
- https://hal.inria.fr/hal-01581090/document
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