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1606.04273
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Metamodel-based sensitivity analysis: Polynomial chaos expansions and Gaussian processes
14 June 2016
Loic Le Gratiet
S. Marelli
Bruno Sudret
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Papers citing
"Metamodel-based sensitivity analysis: Polynomial chaos expansions and Gaussian processes"
11 / 11 papers shown
Title
Surrogate-based global sensitivity analysis with statistical guarantees via floodgate
Massimo Aufiero
Lucas Janson
AI4CE
17
3
0
11 Aug 2022
A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic
Barbara Z Cunha
C. Droz
A. Zine
Stéphane Foulard
M. Ichchou
AI4CE
32
84
0
13 Apr 2022
Extreme learning machines for variance-based global sensitivity analysis
John E. Darges
A. Alexanderian
P. Gremaud
24
2
0
14 Jan 2022
Uncertainty quantification of a three-dimensional in-stent restenosis model with surrogate modelling
Dongwei Ye
Pavel S. Zun
Valeria Krzhizhanovskaya
Alfons G. Hoekstra
22
1
0
11 Nov 2021
Graph Neural Network Guided Local Search for the Traveling Salesperson Problem
Benjamin H. Hudson
Qingbiao Li
Matthew Malencia
Amanda Prorok
25
63
0
11 Oct 2021
Global sensitivity analysis using derivative-based sparse Poincaré chaos expansions
Nora Lüthen
O. Roustant
Fabrice Gamboa
Bertrand Iooss
S. Marelli
Bruno Sudret
21
5
0
01 Jul 2021
Derivative-based global sensitivity analysis for models with high-dimensional inputs and functional outputs
Helen L. Cleaves
A. Alexanderian
H. Guy
Ralph C. Smith
Meilin Yu
17
10
0
12 Feb 2019
Principal component analysis and sparse polynomial chaos expansions for global sensitivity analysis and model calibration: application to urban drainage simulation
J. Nagel
J. Rieckermann
Bruno Sudret
6
63
0
11 Sep 2017
Shapley effects for sensitivity analysis with correlated inputs: comparisons with Sobol' indices, numerical estimation and applications
Bertrand Iooss
Clémentine Prieur
FAtt
25
96
0
05 Jul 2017
Global sensitivity analysis in the context of imprecise probabilities (p-boxes) using sparse polynomial chaos expansions
R. Schöbi
Bruno Sudret
16
60
0
29 May 2017
Global sensitivity analysis using low-rank tensor approximations
K. Konakli
Bruno Sudret
23
77
0
29 May 2016
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