A new computable a posteriori error estimator is introduced, which
relies on the solution of small discrete problems on stars. It exhibits
built-in flux equilibration and is equivalent to the energy error
up to data oscillation without any saturation assumption. A simple
adaptive strategy is designed, which simultaneously reduces error
and data oscillation, and is shown to converge without mesh pre-adaptation
nor explicit knowledge of constants. Numerical experiments reveal
a competitive performance, show extremely good effectivity indices,
and yield quasi-optimal meshes.
%0 Journal Article
%1 morin2003local
%A Morin, Pedro
%A Nochetto, Ricardo H.
%A Siebert, Kunibert G.
%D 2003
%J Mathematics of Computation
%K from:mhartmann ians imported vorlaeufig
%N 243
%P 1067-1097
%R 10.1090/S0025-5718-02-01463-1
%T Local Problems on Stars: A Posteriori Error Estimators, Convergence,
and Performance
%U http://dx.doi.org/10.1090/S0025-5718-02-01463-1
%V 72
%X A new computable a posteriori error estimator is introduced, which
relies on the solution of small discrete problems on stars. It exhibits
built-in flux equilibration and is equivalent to the energy error
up to data oscillation without any saturation assumption. A simple
adaptive strategy is designed, which simultaneously reduces error
and data oscillation, and is shown to converge without mesh pre-adaptation
nor explicit knowledge of constants. Numerical experiments reveal
a competitive performance, show extremely good effectivity indices,
and yield quasi-optimal meshes.
@article{morin2003local,
abstract = {A new computable a posteriori error estimator is introduced, which
relies on the solution of small discrete problems on stars. It exhibits
built-in flux equilibration and is equivalent to the energy error
up to data oscillation without any saturation assumption. A simple
adaptive strategy is designed, which simultaneously reduces error
and data oscillation, and is shown to converge without mesh pre-adaptation
nor explicit knowledge of constants. Numerical experiments reveal
a competitive performance, show extremely good effectivity indices,
and yield quasi-optimal meshes.},
added-at = {2018-07-20T10:54:26.000+0200},
author = {Morin, Pedro and Nochetto, Ricardo H. and Siebert, Kunibert G.},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2f07b09c367476e519a84842a8dbb10b1/mathematik},
doi = {10.1090/S0025-5718-02-01463-1},
interhash = {5218fe1907aac0c236b928826c120882},
intrahash = {f07b09c367476e519a84842a8dbb10b1},
journal = {Mathematics of Computation},
keywords = {from:mhartmann ians imported vorlaeufig},
number = 243,
owner = {kohlsk},
pages = {1067-1097},
timestamp = {2019-12-18T14:37:55.000+0100},
title = {Local Problems on Stars: {A} Posteriori Error Estimators, Convergence,
and Performance},
url = {http://dx.doi.org/10.1090/S0025-5718-02-01463-1},
volume = 72,
year = 2003
}