An efficient and robust adaptive sampling method for polynomial chaos expansion in sparse Bayesian learning framework
type: Item
instance of: scholarly article
title: An efficient and robust adaptive sampling method for polynomial chaos expansion in sparse Bayesian learning framework
zbMATH Open document ID: 1441.62201
author: Yicheng Zhou
author: Kai Cheng
author: Chunyan Ling
author: Zhenzhou Lü
published in: Computer Methods in Applied Mechanics and Engineering
publication date: 2020-04-16T00:00:00Z
Mathematics Subject Classification ID: 62K05
Mathematics Subject Classification ID: 62F15
Mathematics Subject Classification ID: 62-08
zbMATH DE Number: 7189880
zbMATH Keywords: polynomial chaos expansion
zbMATH Keywords: sparse Bayesian learning
zbMATH Keywords: adaptive sampling method
zbMATH Keywords: expectation of loss function
describes a project that uses: SPACE
describes a project that uses: UQLab
MaRDI profile type: Publication
full work available at URL: j.cma.2019.04.046
OpenAlex ID: W2946328771
cites work: Global sensitivity analysis using support vector regression
cites work: Bayesian sparse polynomial chaos expansion for global sensitivity analysis
cites work: Sequential Design of Experiment for Sparse Polynomial Chaos Expansions
cites work: A non-intrusive model reduction approach for polynomial chaos expansion using proper orthogonal decomposition
cites work: Sparse polynomial chaos expansions and adaptive stochastic finite elements using a regression approach
cites work: Adaptive sparse polynomial chaos expansion based on least angle regression
cites work: Efficient uncertainty quantification of stochastic CFD problems using sparse polynomial chaos and compressed sensing
cites work: Enhancing \(\ell_1\)-minimization estimates of polynomial chaos expansions using basis selection
cites work: Sparse polynomial chaos expansions via compressed sensing and D-optimal design
cites work: Polynomial approximation via compressed sensing of high-dimensional functions on lower sets
cites work: A gradient enhanced \(\ell_{1}\)-minimization for sparse approximation of polynomial chaos expansions
cites work: On polynomial chaos expansion via gradient-enhanced \(\ell_1\)-minimization
cites work: Q3093341
cites work: Compressive sampling of polynomial chaos expansions: convergence analysis and sampling strategies
cites work: An Efficient Sampling Method for Regression-Based Polynomial Chaos Expansion
cites work: Nonadaptive quasi-optimal points selection for least squares linear regression
cites work: Least squares polynomial chaos expansion: a review of sampling strategies
cites work: 10.1162/15324430152748236
cites work: Sparse Bayesian Learning for Basis Selection
cites work: Bayesian Compressive Sensing
cites work: Bayesian Compressive Sensing Using Laplace Priors
cites work: Q3997987
cites work: The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
cites work: Practical selection of SVM parameters and noise estimation for SVM regression
cites work: Sequential Design with Mutual Information for Computer Experiments (MICE): Emulation of a Tsunami Model
cites work: Ensembling neural networks: Many could be better than all
cites work: Model selection for small sample regression
cites work: Q3445852
cites work: Inverse regression-based uncertainty quantification algorithms for high-dimensional models: theory and practice
Wikidata QID: Q127836919
DOI: 10.1016/J.CMA.2019.04.046
description: scientific article; zbMATH DE number 7189880