A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness
type: Item
instance of: scholarly article
title: A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness
zbMATH Open document ID: 1441.60004
author: Yanyan Li
published in: Computer Methods in Applied Mechanics and Engineering
publication date: 2020-04-16T00:00:00Z
full work available at URL: https://arxiv.org/abs/1804.08609
full work available at URL: pmc7007047
Mathematics Subject Classification ID: 60-08
Mathematics Subject Classification ID: 62H25
zbMATH DE Number: 7189825
zbMATH Keywords: data-driven
zbMATH Keywords: arbitrary randomness
zbMATH Keywords: mutual dependence
zbMATH Keywords: compressed sensing
zbMATH Keywords: sparsity enhancement
zbMATH Keywords: uncertainty quantification
describes a project that uses: LINCS
describes a project that uses: SETTLE
describes a project that uses: PRMLT
describes a project that uses: Gromacs
describes a project that uses: Chaospy
describes a project that uses: APBS
MaRDI profile type: Publication
OpenAlex ID: W2903666595
Wikidata QID: Q89619317
arXiv ID: 1804.08609
cites work: Q5444939
cites work: Q5483032
cites work: Q4869639
cites work: Multilevel Monte Carlo Methods
cites work: Q4433869
cites work: A continuation multi level Monte Carlo (C-MLMC) method for uncertainty quantification in compressible inviscid aerodynamics
cites work: Accurate Uncertainty Quantification Using Inaccurate Computational Models
cites work: Optimal model management for multifidelity Monte Carlo estimation
cites work: Q4003879
cites work: Monte Carlo and quasi-Monte Carlo methods 1996. Proceedings of a conference at the University of Salzburg, Austria, July 9--12, 1996
cites work: A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
cites work: Large Sample Properties of Simulations Using Latin Hypercube Sampling
cites work: On Latin hypercube sampling
cites work: Design and analysis of computer experiments. With comments and a rejoinder by the authors
cites work: Bayesian calibration of computer models. (With discussion)
cites work: Q3394879
cites work: Q3997987
cites work: The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
cites work: Combining experimental data and computer simulations, with an application to flyer plate experiments
cites work: Modeling uncertainty in flow simulations via generalized polynomial chaos.
cites work: Identification and prediction of stochastic dynamical systems in a polynomial chaos basis
cites work: Uncertainty propagation in CFD using polynomial chaos decomposition
cites work: A generalized polynomial chaos based ensemble Kalman filter with high accuracy
cites work: Q5268658
cites work: Evaluation of failure probability via surrogate models
cites work: POLYNOMIAL-CHAOS-BASED KRIGING
cites work: Comparison of Surrogate-Based Uncertainty Quantification Methods for Computationally Expensive Simulators
cites work: High-Order Collocation Methods for Differential Equations with Random Inputs
cites work: A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data
cites work: A Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data
cites work: An adaptive hierarchical sparse grid collocation algorithm for the solution of stochastic differential equations
cites work: Multi-element probabilistic collocation method in high dimensions
cites work: Sparse pseudospectral approximation method
cites work: A unified framework for mesh refinement in random and physical space
cites work: A non-adapted sparse approximation of PDEs with stochastic inputs
cites work: STOCHASTIC COLLOCATION ALGORITHMS USING l1-MINIMIZATION
cites work: Sparse Legendre expansions via \(\ell_1\)-minimization
cites work: A Compressed Sensing Approach for Partial Differential Equations with Random Input Data
cites work: Reweighted \(\ell_1\) minimization method for stochastic elliptic differential equations
cites work: Compressive sampling of polynomial chaos expansions: convergence analysis and sampling strategies
cites work: On polynomial chaos expansion via gradient-enhanced \(\ell_1\)-minimization
cites work: Sparse Approximation using $\ell_1-\ell_2$ Minimization and Its Application to Stochastic Collocation
cites work: Stochastic Collocation vial1-Minimisation on Low Discrepancy Point Sets with Application to Uncertainty Quantification
cites work: Divide and conquer: an incremental sparsity promoting compressive sampling approach for polynomial chaos expansions
cites work: Sparse polynomial chaos expansions via compressed sensing and D-optimal design
cites work: Compressed sparse tensor based quadrature for vibrational quantum mechanics integrals
cites work: Q5462972
cites work: Constructing Surrogate Models of Complex Systems with Enhanced Sparsity: Quantifying the Influence of Conformational Uncertainty in Biomolecular Solvation
cites work: Active Subspace Methods in Theory and Practice: Applications to Kriging Surfaces
cites work: An adaptive importance sampling algorithm for Bayesian inversion with multimodal distributions
cites work: A probabilistic graphical model based stochastic input model construction
cites work: Multi-Element Generalized Polynomial Chaos for Arbitrary Probability Measures

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cites work: Adaptive multi-element polynomial chaos with discrete measure: algorithms and application to SPDEs

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cites work: An arbitrary polynomial chaos expansion approach for response analysis of acoustic systems with epistemic uncertainty

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cites work: SAMBA: sparse approximation of moment-based arbitrary polynomial chaos

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cites work: Orthogonal Polynomials of Several Variables

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cites work: The restricted isometry property and its implications for compressed sensing

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cites work: Restricted Isometry Constants Where $\ell ^{p}$ Sparse Recovery Can Fail for $0≪ p \leq 1$

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cites work: Stable recovery of sparse overcomplete representations in the presence of noise

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cites work: Decoding by Linear Programming

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cites work: Stable signal recovery from incomplete and inaccurate measurements

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cites work: Sparse Approximate Solutions to Linear Systems

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cites work: Enhancing sparsity of Hermite polynomial expansions by iterative rotations

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cites work: Spectral polynomial chaos solutions of the stochastic advection equation

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cites work: Q3078293

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cites work: Generalized Sobol sensitivity indices for dependent variables: numerical methods

A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness (DOI)
DOI: 10.1016/J.CMA.2019.03.014