Use of global sensitivity analysis and polynomial chaos expansion for interpretation of nonreactive transport experiments in laboratory‐scale porous media

By N. Fajraoui, Fanilo Ramasomanana, Anis Younes, Thierry Alex Mara, Philippe Ackerer, Alberto Guadagnini. 2011

89 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Environmental Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/2abe73bcee8fda521d29c44310a8cdfe2b0cbf9e

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In Civil and Structural Engineering

In Environmental Engineering

In Statistics and Probability

Citations in the corpus

Polynomial chaos expansions for uncertainty propagation and moment independent sensitivity analysis of seawater intrusion simulations (2015, background): “Reference Objective of PCE application Type of problem Number/name of uncertain inputs Uncertain output(s) Type of polynomial Order of PCE(s) Method for selection of data points Method for the estimation of PCE coefficients Li and Zhang (2007) UP Flow in saturated media (1D, 2D/HE) 6, 7/hydraulic co”
Probabilistic assessment of seawater intrusion under multiple sources of uncertainty (2015, background): “The generalized Polynomial Chaos Expansion (gPCE) (e.g. [19,28,36,38,42]) can be employed to build surrogate models of target quantities at a relatively affordable computational cost (see, e.g., [11,15,17] and references therein).”
Efficient posterior exploration of a high‐dimensional groundwater model from two‐stage Markov chain Monte Carlo simulation and polynomial chaos expansion (2013, methodology): “…for stochastic analysis with known parameter distributions [e.g., Li and Zhang, 2007; Li et al., 2009] or global sensitivity analysis [e.g., Fajraoui et al., 2011; Ciriello et al., 2013], but rarely within the context of parameter estimation [some notable exceptions are Saad and Ghanem,…”
Global sensitivity analysis: a flexible and efficient framework with an example from stochastic hydrogeology (2012, background): “Applications in hydrogeological sciences can also be found in the literature [14–16].”
Metamodel-based sensitivity analysis: polynomial chaos expansions and Gaussian processes (2016, unknown): “In the last ﬁve years numerous application examples have been developed using PCE for sensitivity analysis, e.g. Fajraoui et al. [25]; Younes et al. [65]; Brown et al. [14]; Sandoval et al. [45].”