Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark (Q4995117)

scientific article; zbMATH DE number 7362393

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English	Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark	scientific article; zbMATH DE number 7362393

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instance of

scholarly article

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title

Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark (English)

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SIAM/ASA Journal on Uncertainty Quantification

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publication date

23 June 2021

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full work available at URL

https://arxiv.org/abs/2002.01290

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zbMATH Keywords

uncertainty quantification

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surrogate modelling

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sparse regression

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sparse polynomial chaos expansions

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experimental design

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describes a project that uses

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A robust and efficient stepwise regression method for building sparse polynomial chaos expansions

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Divide and conquer: an incremental sparsity promoting compressive sampling approach for polynomial chaos expansions

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A near-optimal sampling strategy for sparse recovery of polynomial chaos expansions

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A preconditioning approach for improved estimation of sparse polynomial chaos expansions

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Bayesian Compressive Sensing Using Laplace Priors

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Some greedy algorithms for sparse polynomial chaos expansions

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Stochastic finite element: a non intrusive approach by regression

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Global sensitivity analysis: a Bayesian learning based polynomial chaos approach

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Nonmonotone Spectral Projected Gradient Methods on Convex Sets

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Sparse polynomial chaos expansions and adaptive stochastic finite elements using a regression approach

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Adaptive sparse polynomial chaos expansion based on least angle regression

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From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images

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Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information

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A Probabilistic and RIPless Theory of Compressed Sensing

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Enhancing sparsity by reweighted $\ell _{1}$ minimization

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Model selection for small sample regression

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Optimal weighted least-squares methods

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A Comparison of Algorithms for Constructing Exact D-Optimal Designs

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Subspace Pursuit for Compressive Sensing Signal Reconstruction

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Sparse polynomial chaos expansions via compressed sensing and D-optimal design

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Compressed sensing

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A non-adapted sparse approximation of PDEs with stochastic inputs

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Least angle regression. (With discussion)

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On the convergence of generalized polynomial chaos expansions

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Sequential Design of Experiment for Sparse Polynomial Chaos Expansions

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Wavelet-based image estimation: an empirical Bayes approach using Jeffrey's noninformative prior

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Q3997987

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Efficient Algorithms for Computing a Strong Rank-Revealing QR Factorization

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Sparse Approximation of Data-Driven Polynomial Chaos Expansions: An Induced Sampling Approach

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Stochastic Collocation Methods via $\ell_1$ Minimization Using Randomized Quadratures

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Least squares polynomial chaos expansion: a review of sampling strategies

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On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals

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Coherence motivated sampling and convergence analysis of least squares polynomial chaos regression

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Compressive sampling of polynomial chaos expansions: convergence analysis and sampling strategies

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Basis adaptive sample efficient polynomial chaos (BASE-PC)

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The elements of statistical learning. Data mining, inference, and prediction

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Rank-Revealing QR Factorizations and the Singular Value Decomposition

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Adaptive-sparse polynomial chaos expansion for reliability analysis and design of complex engineering systems

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Sequential Design for Ranking Response Surfaces

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Compressive Sensing with Cross-Validation and Stop-Sampling for Sparse Polynomial Chaos Expansions

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Enhancing $\ell_1$-minimization estimates of polynomial chaos expansions using basis selection

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A Generalized Sampling and Preconditioning Scheme for Sparse Approximation of Polynomial Chaos Expansions

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Bayesian Compressive Sensing

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Optimum Designs in Regression Problems

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Polynomial meta-models with canonical low-rank approximations: numerical insights and comparison to sparse polynomial chaos expansions

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EXTENDING CLASSICAL SURROGATE MODELING TO HIGH DIMENSIONS THROUGH SUPERVISED DIMENSIONALITY REDUCTION: A DATA-DRIVEN APPROACH

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D-MORPH regression: Application to modeling with unknown parameters more than observation data

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SURROGATE MODELING OF INDOOR DOWN-LINK HUMAN EXPOSURE BASED ON SPARSE POLYNOMIAL CHAOS EXPANSION

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STOCHASTIC SPECTRAL EMBEDDING

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A Compressed Sensing Approach for Partial Differential Equations with Random Input Data

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A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code

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Rectangular maximum-volume submatrices and their applications

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A Christoffel function weighted least squares algorithm for collocation approximations

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CoSaMP: Iterative signal recovery from incomplete and inaccurate samples

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A review of some exchange algorithms for constructing discrete $D$-optimal designs

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Controlling Correlations in Latin Hypercube Samples

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PLS-based adaptation for efficient PCE representation in high dimensions

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A weighted $\ell_1$-minimization approach for sparse polynomial chaos expansions

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Grid and basis adaptive polynomial chaos techniques for sensitivity and uncertainty analysis

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Design of computer experiments: space filling and beyond

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Sparse Legendre expansions via $\ell_1$-minimization

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DIMENSIONALITY REDUCTION FOR COMPLEX MODELS VIA BAYESIAN COMPRESSIVE SENSING

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POLYNOMIAL-CHAOS-BASED KRIGING

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Effectively Subsampled Quadratures for Least Squares Polynomial Approximations

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Active Learning

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Bayesian sparse polynomial chaos expansion for global sensitivity analysis

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Nonadaptive quasi-optimal points selection for least squares linear regression

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On a near optimal sampling strategy for least squares polynomial regression

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On the distribution of points in a cube and the approximate evaluation of integrals

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Subsampled Gauss Quadrature Nodes for Estimating Polynomial Chaos Expansions

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Regression-based sparse polynomial chaos for uncertainty quantification of subsurface flow models

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Basis adaptation in homogeneous chaos spaces

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10.1162/15324430152748236

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Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit

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Compressive sensing adaptation for polynomial chaos expansions

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Sparse polynomial chaos expansions using variational relevance vector machines

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Probing the Pareto Frontier for Basis Pursuit Solutions

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Sparse Bayesian Learning for Basis Selection

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High-Order Collocation Methods for Differential Equations with Random Inputs

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The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations

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STOCHASTIC COLLOCATION ALGORITHMS USING l1-MINIMIZATION

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Reweighted $\ell_1$ minimization method for stochastic elliptic differential equations

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Sliced-Inverse-Regression--Aided Rotated Compressive Sensing Method for Uncertainty Quantification

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Minimization of $\ell_{1-2}$ for Compressed Sensing

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An Efficient Sampling Method for Regression-Based Polynomial Chaos Expansion

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An efficient adaptive forward-backward selection method for sparse polynomial chaos expansion

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Multivariate Discrete Least-Squares Approximations with a New Type of Collocation Grid

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An efficient and robust adaptive sampling method for polynomial chaos expansion in sparse Bayesian learning framework

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Surrogate modeling of high-dimensional problems via data-driven polynomial chaos expansions and sparse partial least square

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REPLICATION-BASED EMULATION OF THE RESPONSE DISTRIBUTION OF STOCHASTIC SIMULATORS USING GENERALIZED LAMBDA DISTRIBUTIONS

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Identifiers

zbMATH Open document ID

1464.65008

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DOI

10.1137/20M1315774

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Mathematics Subject Classification ID

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Sitelinks

Mathematics(1 entry)

mardi Publication:4995117