An efficient metamodeling approach for uncertainty quantification of complex systems with arbitrary parameter probability distributions
type: Item
instance of: scholarly article
title: An efficient metamodeling approach for uncertainty quantification of complex systems with arbitrary parameter probability distributions
zbMATH Open document ID: 1548.6208
author: Q6092234
author: Wei-Xin Ren
author: Michael D. Todd
published in: International Journal for Numerical Methods in Engineering
publication date: 2024-07-01T00:00:00Z
Mathematics Subject Classification ID: 62F15
Mathematics Subject Classification ID: 62G99
Mathematics Subject Classification ID: 62P30
zbMATH DE Number: 7874321
zbMATH Keywords: uncertainty quantification
zbMATH Keywords: parameter uncertainty
zbMATH Keywords: Gaussian process model
zbMATH Keywords: arbitrary probability distribution
zbMATH Keywords: Gaussian quadrature
MaRDI profile type: Publication
cites work: Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index
cites work: Computational procedure for a fast calculation of eigenvectors and eigenvalues of structures with random properties
cites work: The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
cites work: Galerkin Finite Element Approximations of Stochastic Elliptic Partial Differential Equations
cites work: Galerkin methods for linear and nonlinear elliptic stochastic partial differential equations
cites work: Uncertainty Quantification and Polynomial Chaos Techniques in Computational Fluid Dynamics
cites work: An invariant subspace‐based approach to the random eigenvalue problem of systems with clustered spectrum
cites work: A spectral approach for fuzzy uncertainty propagation in finite element analysis
cites work: Uncertainty quantification of high-dimensional complex systems by multiplicative polynomial dimensional decompositions
cites work: Q3583848
cites work: An adaptive hierarchical sparse grid collocation algorithm for the solution of stochastic differential equations
cites work: Adaptive-sparse polynomial dimensional decomposition methods for high-dimensional stochastic computing
cites work: Selection of polynomial chaos bases via Bayesian model uncertainty methods with applications to sparse approximation of PDEs with stochastic inputs
cites work: Gradient-based methods for uncertainty quantification in hypersonic flows
cites work: Multi-output separable Gaussian process: towards an efficient, fully Bayesian paradigm for uncertainty quantification
cites work: Gaussian process emulators for the stochastic finite element method
cites work: A hybrid spectral and metamodeling approach for the stochastic finite element analysis of structural dynamic systems
cites work: Q3394879
cites work: Q4820343
cites work: The numerically stable reconstruction of Jacobi matrices from spectral data
cites work: Fast construction of the Fejér and Clenshaw-Curtis quadrature rules
cites work: On Generating Orthogonal Polynomials
DOI: 10.1002/NME.5305
description: scientific article; zbMATH DE number 7874321