Adaptive sparse polynomial chaos expansion based on least angle regression

By Géraud Blatman, Bruno Sudret. Journal of Computational Physics, 2011

1,454 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Polynomial chaos expansion with random and fuzzy variables (2016, methodology): “made the PCE technique possible for a large number of uncertain parameters, by using an adaptive sparse PCE [13,14].”
Polynomial chaos expansion in structural dynamics: Accelerating the convergence of the first two statistical moment sequences (2015, background): “Several novel approaches have been developed to address the issue of computational cost, see for example [19–23].”
Polynomial-Chaos-based Kriging (2015, positive): “The set of polynomials is determined using the least-angle-regression (LAR) algorithm as in [32] together with hyperbolic index sets to obtain sparse sets of polynomials.”
Arbitrary polynomial chaos expansion method for uncertainty quantification and global sensitivity analysis in structural dynamics (2020, background): “Fortunately, the sparse grid and/or adaptive schemes can provide an 404 effective remedy to alleviate the “curse of dimensionality” [54, 61].”
Metamodel-based sensitivity analysis: polynomial chaos expansions and Gaussian processes (2016, neutral): “Blatman and Sudret [13]; Doostan and Owhadi [21].”
Seismic fragility curves for structures using non-parametric representations (2017, background): “Polynomial chaos (PC) expansions [75,76] appear as a promising tool.”