ANOVA kernels and RKHS of zero mean functions for model-based sensitivity analysis
By Nicolas Durrande, David Ginsbourger, O. Roustant, Laurent Carraro. 2011
90 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Mechanics of Materials, Statistics and Probability
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
- Paris Perdikaris
- G. Em Karniadakis
- Chris J. Oates
- Nicolas Chopin
- Mark A. Girolami
- Daniele Venturi
- Yibo Yang
- Maizar Raissi
In Mechanics of Materials
In Statistics and Probability
- Paris Perdikaris
- Chris J. Oates
- Mark A. Girolami
- G. Em Karniadakis
- Nicolas Chopin
- Daniele Venturi
- Qiang Liu
- Youssef M. Marzouk
Citations in the corpus
- Control functionals for Monte Carlo integration (2014, background): “sists of only valid control functionals, i.e. 2H 0 =)( ) = 0.These ideas are illustrated in Fig. 1. Remark 4. An alternative kernel construction for spaces of functions with ( ) = 0 was proposed in Durrande et al. (2013, Eqn. 20) in the context of ANOVA, but this does not generally produce closed-fo”
- Multifidelity Information Fusion Algorithms for High-Dimensional Systems and Massive Data sets (2016, background): “To this end, we adopt the approach of [32] that employs an orthonormal basis of shifted Legendre polynomials (up to order 15), using adaptive criteria for the optimal selection of the polynomial order that approximates each component function, and variance reduction techniques that enhance the accur”
- Polynomial-Chaos-based Kriging (2015, unknown): “…in the aspects of optimal estimation of the hyper-parameters Bachoc (2013b); Bachoc et al. (2014); Bachoc (2013a), the use of adaptive kernels Duvenaud et al. (2011); Ginsbourger et al. (2013) and the use of additive auto-correlation kernels Durrande et al. (2012, 2013); Ginsbourger et al. (2013)”