SAMBA: sparse approximation of moment-based arbitrary polynomial chaos

DOI10.1016/j.jcp.2016.05.014zbMath1349.65417OpenAlexW2376314300MaRDI QIDQ726888

B. Belkouchi, Francesco Montomoli, Richard Ahlfeld

Publication date: 5 December 2016

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10044/1/32946

zbMATH Keywords

SAMBAuncertainty quantificationanisotropic Smolyak gridarbitrary polynomial chaosnon-intrusive polynomial chaossparse Gaussian quadrature

Mathematics Subject Classification ID

Approximations to statistical distributions (nonasymptotic) (62E17) Mesh generation, refinement, and adaptive methods for the numerical solution of initial value and initial-boundary value problems involving PDEs (65M50)

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Uses Software

• ORTHPOL

Cites Work

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• Robust design with arbitrary distributions using Gauss-type quadrature formula
• Sparse pseudospectral approximation method
• Explicit cost bounds of algorithms for multivariate tensor product problems
• Modeling uncertainty in flow simulations via generalized polynomial chaos.
• High dimensional polynomial interpolation on sparse grids
• Smolyak method for solving dynamic economic models: Lagrange interpolation, anisotropic grid and adaptive domain
• Beyond Wiener-Askey expansions: handling arbitrary PDFs
• On the convergence of generalized polynomial chaos expansions
• Multi-Element Generalized Polynomial Chaos for Arbitrary Probability Measures
• Some basic hypergeometric orthogonal polynomials that generalize Jacobi polynomials
• Algorithm 726: ORTHPOL–a package of routines for generating orthogonal polynomials and Gauss-type quadrature rules
• Physical Systems with Random Uncertainties: Chaos Representations with Arbitrary Probability Measure
• Efficient statistical tolerance analysis for general distributions using three-point information
• The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
• Adaptive Smolyak Pseudospectral Approximations
• Calculation of Gauss Quadrature Rules

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