Efficient uncertainty quantification of stochastic CFD problems using sparse polynomial chaos and compressed sensing

DOI10.1016/j.compfluid.2017.06.016zbMath1390.76801OpenAlexW2642249342MaRDI QIDQ1648406

Saeed Salehi, Ahmad Nourbakhsh, Michel J. Cervantes, Mehrdad Raisee

Publication date: 27 June 2018

Published in: Computers and Fluids (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.compfluid.2017.06.016

zbMATH Keywords

uncertainty quantificationcompressed sensingorthogonal matching pursuit\(\ell_{1}\)-minimizationsparse polynomial chaos expansionarbitrarily distributed uncertainties

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Stochastic analysis applied to problems in fluid mechanics (76M35) PDEs with randomness, stochastic partial differential equations (35R60) Computational methods for stochastic equations (aspects of stochastic analysis) (60H35)

Related Items (8)

Stagnation point heat flux characterization under numerical error and boundary conditions uncertainty ⋮ Multi-level multi-fidelity sparse polynomial chaos expansion based on Gaussian process regression ⋮ An active sparse polynomial chaos expansion approach based on sequential relevance vector machine ⋮ An efficient and robust adaptive sampling method for polynomial chaos expansion in sparse Bayesian learning framework ⋮ Efficient uncertainty quantification of CFD problems by combination of proper orthogonal decomposition and compressed sensing ⋮ On the flow field and performance of a centrifugal pump under operational and geometrical uncertainties ⋮ An efficient multifidelity \(\ell_1\)-minimization method for sparse polynomial chaos ⋮ Efficient aerodynamic analysis and optimization under uncertainty using multi-fidelity polynomial chaos-Kriging surrogate model

Uses Software

• TESTPACK
• Spalart-Allmaras

Cites Work

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