Discrete least squares polynomial approximation with random evaluations − application to parametric and stochastic elliptic PDEs
By Abdellah Chkifa, Albert Cohen, Giovanni Migliorati, Fabio Nobile, Raúl Tempone. 2015
122 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Environmental Engineering, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/52d69668663ab66f3ea8cf9344f6ffb2cd7a6313
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
- Ronald A. DeVore
- Albert Cohen
- Jerrad Hampton
- Alireza Doostan
- Christoph Schwab
- Rob P. Stevenson
- Eric Moulines
- Murad S. Taqqu
In Environmental Engineering
In Statistics and Probability
- Albert Cohen
- Ronald A. DeVore
- Jerrad Hampton
- Alireza Doostan
- Christoph Schwab
- Claude Jeffrey Gittelson
- Anthony Nouy
- Mohammad Hadigol
Citations in the corpus
- Coherence motivated sampling and convergence analysis of least squares polynomial Chaos regression (2014, background): “Bounds on the expected mean squares error were derived in [9, 11] for bounded orthogonal polynomials when sampled according to their natural orthogonality measure.”
- Approximation of high-dimensional parametric PDEs * (2015, methodology): “Other non-intrusive methods are based on least-square regression as discussed in [16, 34, 35, 69], or on pseudo-spectral approximation as discussed in [91, 26].”
- Polynomial-Chaos-based Kriging (2015, unknown): “Different nonintrusive methods have been proposed in the last decade to calibrate PC meta-models, namely projection [20–22], stochastic collocation [23, 24], and least-square minimization methods [26, 29, 32, 54, 55].”