Reduced basis' acquisition by a learning process for rapid on-line approximation of solution to PDE's: laminar flow past a backstep

DOI10.1007/s11831-017-9238-zzbMath1391.76328OpenAlexW2745141280WikidataQ113322980 ScholiaQ113322980MaRDI QIDQ1639597

Patrick Gallinari, Olivier Schwander, Yvon Maday, Maxime Sangnier, Tommaso Taddei

Publication date: 13 June 2018

Published in: Archives of Computational Methods in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11831-017-9238-z

Mathematics Subject Classification ID

Navier-Stokes equations for incompressible viscous fluids (76D05) Finite element, Rayleigh-Ritz and Galerkin methods for boundary value problems involving PDEs (65N30) Finite element methods applied to problems in fluid mechanics (76M10)

Related Items (4)

A stabilized POD model for turbulent flows over a range of Reynolds numbers: optimal parameter sampling and constrained projection ⋮ Registration-based model reduction in complex two-dimensional geometries ⋮ Bayesian Model and Dimension Reduction for Uncertainty Propagation: Applications in Random Media ⋮ Space-time registration-based model reduction of parameterized one-dimensional hyperbolic PDEs

Uses Software

• ElemStatLearn
• redbKIT

Cites Work

• Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive partial differential equations. Application to transport and continuum mechanics.
• A static condensation reduced basis element method: complex problems
• Optimal Local Approximation Spaces for Component-Based Static Condensation Procedures
• A Static condensation Reduced Basis Element method : approximation anda posteriorierror estimation
• Certified Reduced Basis Methods for Parametrized Partial Differential Equations
• A reduced basis element method for the steady Stokes problem
• The Reduced Basis Element Method: Application to a Thermal Fin Problem
• Reduced Basis Methods for Partial Differential Equations

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