Practical Approaches To Construct RS-HDMR Component Functions
By Genyuan Li, Sheng-Wei Wang, Herschel A. Rabitz. 2002
289 citations recorded by the source corpus.
Subfields: Atmospheric Science, Environmental Engineering
View Paper: https://www.semanticscholar.org/paper/7d7939f8936e69a3ad42a1fee4be80a48f59a13a
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Atmospheric Science
- G. Em Karniadakis
- Paris Perdikaris
- Sharif Rahman
- Zhike Zi
- Marco Ratto
- Anders Grimvall
- Davood Shahsavani
- Daniele Venturi
In Environmental Engineering
- Sharif Rahman
- Marco Ratto
- Zhike Zi
- Paris Perdikaris
- G. Em Karniadakis
- Daniele Venturi
- Anders Grimvall
- Davood Shahsavani
Citations in the corpus
- Global uncertainty assessments by high dimensional model representations (HDMR) (2002, background): “The coe8cient i k and ij kl can be determined by minimization of the functionals (Li et al., 2002)”
- Analysing DSGE Models with Global Sensitivity Analysis (2008, methodology): “(RS-HDMR) ( Li et al. 2002, 2006 ), where the HDMR terms are estimated by interpolating with orthogonal polynomials a Monte Carlo sample of the mapping Y = f( X1 ,...,X k). Oakley and O’Hagan (2004) do a similar job as Li et al. by applying Gaussian emulators.”
- Variance-based sensitivity analysis of model outputs using surrogate models (2011, methodology): “Compared to previously suggested techniques for approximating the model output at untried inputs (Li et al., 2002, 2006; Oakley and O’Hagan, 2004; Ratto et al., 2006; Storlie and Helton, 2007), our method has the advantage of being adaptable to almost any local nonlinear features of the output.”
- Extended Polynomial Dimensional Decomposition for Arbitrary Probability Distributions (2009, methodology): “The decomposition, also known as analysis of variance ANOVA Efron and Stein 1981 or high-dimensional model representation HDMR Li et al. 2002; Rabitz and Alis 1999; Sobol 2003 , represents a finite, hierarchical, and convergent expansion of a multivariate output function Rahman 2008 y x = y0 +…”
- Multifidelity Information Fusion Algorithms for High-Dimensional Systems and Massive Data sets (2016, methodology): “Given a set of randomly sampled scattered observations, the mutually orthogonal component functions are typically computed using Monte Carlo and its variants, or probabilistic collocation methods [28, 29].”
- Sensitivity analysis approaches applied to systems biology models. (2011, methodology): “The approaches for calculating RS-HDMR component function were described in the previous publications [67, 69–71].”