ON THE OPTIMAL POLYNOMIAL APPROXIMATION OF STOCHASTIC PDES BY GALERKIN AND COLLOCATION METHODS
By Joakim Beck, Raúl Tempone, Fabio Nobile, Lorenzo Tamellini. 2012
159 citations recorded by the source corpus.
Subfields: Computational Mechanics, Numerical Analysis, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/5c9a9892c7c74df763dcbc676ee3e098f3d757cb
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- Albert Cohen
- Ronald A. DeVore
- Christoph Schwab
- Bernhard Wieland
- Rob P. Stevenson
- Vladimir N. Temlyakov
- Wolfgang Dahmen
- Martin J. Wainwright
In Numerical Analysis
- Christoph Schwab
- Albert Cohen
- Ronald A. DeVore
- Viet Ha Hoang
- Bernhard Wieland
- Claude Jeffrey Gittelson
- Andrew M. Stuart
- Bernardo Cockburn
In Statistics and Probability
- Christoph Schwab
- Albert Cohen
- Ronald A. DeVore
- Viet Ha Hoang
- Bernhard Wieland
- Claude Jeffrey Gittelson
- Andrew M. Stuart
- Boris N. Khoromskij
Citations in the corpus
- Reduced basis methods for partial differential equations with stochastic influences (2013, methodology): “In this section, we briefly describe the idea of stochastic collocation methods [6, 9, 10, 102] that can be seen as a generalization of the stochastic Galerkin method.”
- Approximation of high-dimensional parametric PDEs * (2015, methodology): “Other intrusive methods based on Galerkin projection are discussed in [4, 5, 23, 42].”
- N-term Wiener chaos approximation rates for elliptic PDEs with lognormal Gaussian random inputs (2014, background): “4) now follows from the inequality (see [4, 14]) dn ≤ n!/(loge 2) which holds for all n ∈ N0.”