An efficient metamodeling approach for uncertainty quantification of complex systems with arbitrary parameter probability distributions
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Publication:6565211
DOI10.1002/nme.5305zbMATH Open1548.6208MaRDI QIDQ6565211
Unnamed Author, Wei-Xin Ren, Michael D. Todd
Publication date: 1 July 2024
Published in: International Journal for Numerical Methods in Engineering (Search for Journal in Brave)
Gaussian quadratureparameter uncertaintyuncertainty quantificationarbitrary probability distributionGaussian process model
Bayesian inference (62F15) Applications of statistics in engineering and industry; control charts (62P30) Nonparametric inference (62G99)
Cites Work
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- Multi-output separable Gaussian process: towards an efficient, fully Bayesian paradigm for uncertainty quantification
- Selection of polynomial chaos bases via Bayesian model uncertainty methods with applications to sparse approximation of PDEs with stochastic inputs
- Adaptive-sparse polynomial dimensional decomposition methods for high-dimensional stochastic computing
- A hybrid spectral and metamodeling approach for the stochastic finite element analysis of structural dynamic systems
- Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index
- The numerically stable reconstruction of Jacobi matrices from spectral data
- Galerkin methods for linear and nonlinear elliptic stochastic partial differential equations
- An adaptive hierarchical sparse grid collocation algorithm for the solution of stochastic differential equations
- Gradient-based methods for uncertainty quantification in hypersonic flows
- A spectral approach for fuzzy uncertainty propagation in finite element analysis
- Fast construction of the Fejér and Clenshaw-Curtis quadrature rules
- Gaussian process emulators for the stochastic finite element method
- Uncertainty quantification of high-dimensional complex systems by multiplicative polynomial dimensional decompositions
- Uncertainty Quantification and Polynomial Chaos Techniques in Computational Fluid Dynamics
- On Generating Orthogonal Polynomials
- Galerkin Finite Element Approximations of Stochastic Elliptic Partial Differential Equations
- The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
- An invariant subspace‐based approach to the random eigenvalue problem of systems with clustered spectrum
- Computational procedure for a fast calculation of eigenvectors and eigenvalues of structures with random properties
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