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Global Sensitivity Analysis via Multi-Fidelity Polynomial Chaos
  Expansion

Global Sensitivity Analysis via Multi-Fidelity Polynomial Chaos Expansion

Reliability Engineering & System Safety (Reliab. Eng. Syst. Saf.), 2017
23 October 2017
P. Palar
L. Zuhal
K. Shimoyama
T. Tsuchiya
ArXiv (abs)PDFHTML

Papers citing "Global Sensitivity Analysis via Multi-Fidelity Polynomial Chaos Expansion"

5 / 5 papers shown
Assessing the performance of correlation-based multi-fidelity neural emulators
Assessing the performance of correlation-based multi-fidelity neural emulators
Cristian J. Villatoro
Gianluca Geraci
Daniele E. Schiavazzi
171
1
0
02 Dec 2025
Multi-fidelity Machine Learning for Uncertainty Quantification and
  Optimization
Multi-fidelity Machine Learning for Uncertainty Quantification and OptimizationJournal of Machine Learning for Modeling and Computing (JMLMC), 2024
Ruda Zhang
Negin Alemazkoor
AI4CE
263
9
0
30 Oct 2024
A Bayesian neural network approach to Multi-fidelity surrogate modelling
A Bayesian neural network approach to Multi-fidelity surrogate modellingInternational Journal for Uncertainty Quantification (IJUQ), 2023
Baptiste Kerleguer
C. Cannamela
Josselin Garnier
UQCV
201
7
0
05 Dec 2023
Deep Adaptive Arbitrary Polynomial Chaos Expansion: A Mini-data-driven
  Semi-supervised Method for Uncertainty Quantification
Deep Adaptive Arbitrary Polynomial Chaos Expansion: A Mini-data-driven Semi-supervised Method for Uncertainty QuantificationReliability Engineering & System Safety (Reliab. Eng. Syst. Saf.), 2021
Wen Yao
Xiaohu Zheng
Jun Zhang
Ning Wang
Guijian Tang
224
39
0
22 Jul 2021
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
114
3
0
20 Jan 2021
1
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