Basis Construction for Reduced Basis Methods By Adaptive Parameter Grids
B. Haasdonk, and M. Ohlberger. Proc. International Conference on Adaptive Modeling and Simulation, ADMOS 2007, CIMNE, Barcelona, (2007)
Abstract
An important step in parametrized PDE-based model reduction with reduced
basis methods is the generation of a reduced basis space, on which
the detailed numerical simula- tions are projected. We present a
new strategy for this reduced basis generation. We apply an effective
exploration of the parameter space by adaptive grids. The resulting
method gives a considerable improvement concerning equal distribution
of the model error over the parameter space compared to ï¬xed or
uniformly reï¬ned grids. It is computationally very effective in
terms of small ratio of training-time and model-error.
%0 Conference Paper
%1 HO07
%A Haasdonk, Bernard
%A Ohlberger, M.
%B Proc. International Conference on Adaptive Modeling and Simulation, ADMOS 2007
%D 2007
%E Díez, P.
%E Runesson, K.
%I CIMNE, Barcelona
%K imported from:britsteiner ians anm
%T Basis Construction for Reduced Basis Methods By Adaptive Parameter Grids
%X An important step in parametrized PDE-based model reduction with reduced
basis methods is the generation of a reduced basis space, on which
the detailed numerical simula- tions are projected. We present a
new strategy for this reduced basis generation. We apply an effective
exploration of the parameter space by adaptive grids. The resulting
method gives a considerable improvement concerning equal distribution
of the model error over the parameter space compared to ï¬xed or
uniformly reï¬ned grids. It is computationally very effective in
terms of small ratio of training-time and model-error.
@inproceedings{HO07,
abstract = {An important step in parametrized PDE-based model reduction with reduced
basis methods is the generation of a reduced basis space, on which
the detailed numerical simula- tions are projected. We present a
new strategy for this reduced basis generation. We apply an effective
exploration of the parameter space by adaptive grids. The resulting
method gives a considerable improvement concerning equal distribution
of the model error over the parameter space compared to ï¬xed or
uniformly reï¬ned grids. It is computationally very effective in
terms of small ratio of training-time and model-error.},
added-at = {2021-09-29T14:35:07.000+0200},
author = {Haasdonk, Bernard and Ohlberger, M.},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/235192fc6e642f57a5372eb2e6070af97/mathematik},
booktitle = {Proc. International Conference on Adaptive Modeling and Simulation, ADMOS 2007},
editor = {D\'iez, P. and Runesson, K.},
file = {:PDF/UNSORTED/confidential_HO07_Haasdonk_Ohlberger_ADMOS2007_proof.pdf:PDF},
groups = {haasdonk, haasdonk_all_papers},
interhash = {2fd5bb83aa564299c980c68788022074},
intrahash = {35192fc6e642f57a5372eb2e6070af97},
keywords = {imported from:britsteiner ians anm},
owner = {haasdonk},
publisher = {CIMNE, Barcelona},
timestamp = {2021-09-29T12:35:07.000+0200},
title = {Basis Construction for Reduced Basis Methods By Adaptive Parameter Grids},
year = 2007
}