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Using sparse polynomial chaos expansions for the global sensitivity
  analysis of groundwater lifetime expectancy in a multi-layered
  hydrogeological model
v1v2v3 (latest)

Using sparse polynomial chaos expansions for the global sensitivity analysis of groundwater lifetime expectancy in a multi-layered hydrogeological model

5 February 2015
G. Deman
K. Konakli
Bruno Sudret
J. Kerrou
P. Perrochet
H. Benabderrahmane
ArXiv (abs)PDFHTML

Papers citing "Using sparse polynomial chaos expansions for the global sensitivity analysis of groundwater lifetime expectancy in a multi-layered hydrogeological model"

4 / 4 papers shown
Data-driven sparse polynomial chaos expansion for models with dependent
  inputs
Data-driven sparse polynomial chaos expansion for models with dependent inputsSocial Science Research Network (SSRN), 2021
Zhanlin Liu
Youngjun Choe
119
3
0
20 Jan 2021
Surrogate-Based Bayesian Inverse Modeling of the Hydrological System: An
  Adaptive Approach Considering Surrogate Approximation Error
Surrogate-Based Bayesian Inverse Modeling of the Hydrological System: An Adaptive Approach Considering Surrogate Approximation Error
Jiangjiang Zhang
Q. Zheng
Dingjiang Chen
Laosheng Wu
L. Zeng
317
42
0
10 Jul 2018
Reliability analysis of high-dimensional models using low-rank tensor
  approximations
Reliability analysis of high-dimensional models using low-rank tensor approximations
K. Konakli
Bruno Sudret
188
48
0
28 Jun 2016
Global sensitivity analysis using low-rank tensor approximations
Global sensitivity analysis using low-rank tensor approximations
K. Konakli
Bruno Sudret
169
83
0
29 May 2016
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