%0 Conference Paper %1 Kaulmann2013 %A Kaulmann, Sven %A Haasdonk, Bernard %B VI International Conference on Adaptive Modeling and Simulation (ADMOS 2013) %C Lisbon, Portugal %D 2013 %E Moitinho de Almeida, José Paulo Baptista %E D\'ıez, Pedro %E Tiago, Carlos %E Parés, Núria %K anm ians imported %P 365--376 %T Online Greedy Reduced Basis Construction Using Dictionaries %U http://www.lacan.upc.edu/admos2013/Proceedings.html %X The Reduced Basis method is a means for model order reduction for parametrized partial differential equations. In the last decades it has found broad application for problems with multi-query or real-time character. While the method has shown to be performing well for numerous different fields of applications, problems with high parameter dimension or high sensitivity with respect to the parameter still pose major challenges. In our contribution, we present a new basis generation algorithm that is particularly fit to these kinds of problems: Instead of building the reduced basis during the offline phase we build a large dictionary of basis vector candidates and compute a small parameter-adapted basis from that dictionary with a Greedy procedure during the online phase.

@inproceedings{Kaulmann2013, abstract = {The Reduced Basis method is a means for model order reduction for parametrized partial differential equations. In the last decades it has found broad application for problems with multi-query or real-time character. While the method has shown to be performing well for numerous different fields of applications, problems with high parameter dimension or high sensitivity with respect to the parameter still pose major challenges. In our contribution, we present a new basis generation algorithm that is particularly fit to these kinds of problems: Instead of building the reduced basis during the offline phase we build a large dictionary of basis vector candidates and compute a small parameter-adapted basis from that dictionary with a Greedy procedure during the online phase.}, added-at = {2021-09-29T14:33:27.000+0200}, address = {Lisbon, Portugal}, author = {Kaulmann, Sven and Haasdonk, Bernard}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2051727b1b99af35bbc8d8c478f84e0e6/britsteiner}, booktitle = {VI International Conference on Adaptive Modeling and Simulation (ADMOS 2013)}, date-added = {2014-01-07T08:14:37GMT}, date-modified = {2014-06-03T08:55:20GMT}, editor = {Moitinho de Almeida, Jos{\'e} Paulo Baptista and D{\'\i}ez, Pedro and Tiago, Carlos and Par{\'e}s, N{\'u}ria}, file = {:PDF/Kaulmann2013a.pdf:PDF}, groups = {haasdonk, haasdonk_all_papers}, interhash = {994e3f09ef3708dd18cd4708d2650adb}, intrahash = {051727b1b99af35bbc8d8c478f84e0e6}, keywords = {anm ians imported}, month = may, owner = {kaulmann}, pages = {365--376}, rating = {0}, read = {Yes}, timestamp = {2021-09-29T12:35:04.000+0200}, title = {{Online Greedy Reduced Basis Construction Using Dictionaries}}, url = {http://www.lacan.upc.edu/admos2013/Proceedings.html}, year = 2013 }