Talks and Poster Presentations (without Proceedings-Entry):
A. Bespalov, D. Praetorius, M. Ruggeri:
"Error estimation and adaptive algorithms for multilevel stochastic Galerkin FEM";
Talk: 20th Biennial Computational Techniques and Applications Conference (CTAC 2020),
We consider a class of parametric elliptic PDEs, where the coefficients have affine dependence on infinitely many (uncertain) parameters. We introduce a two-level a posteriori estimator to control the energy error in multilevel stochastic Galerkin approximations. We show that the two level estimator always provides a lower bound for the unknown approximation error, while the upper bound is equivalent to a saturation assumption. We propose and numerically compare adaptive algorithms, where the structure of the estimator is exploited to perform spatial refinement and parametric enrichment.
Created from the Publication Database of the Vienna University of Technology.