Nonlinear model-order reduction for compressible flow solvers using the discrete empirical interpolation method

DOI10.1016/j.jcp.2016.08.004zbMath1371.76103OpenAlexW2517554629WikidataQ59870477 ScholiaQ59870477MaRDI QIDQ525938

Miguel Fosas de Pando, Peter J. Schmid, Denis Sipp

Publication date: 8 May 2017

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

Full work available at URL: http://hdl.handle.net/10044/1/39080

zbMATH Keywords

proper orthogonal decompositioncompressible flowsreduced-order modelsaeroacousticsdiscrete empirical interpolation method

Mathematics Subject Classification ID

Related Items (8)

Acceleration of the spectral stochastic FEM using POD and element based discrete empirical approximation for a micromechanical model of heterogeneous materials with random geometry ⋮ Nonlinear model reduction: a comparison between POD-Galerkin and POD-DEIM methods ⋮ A reduced order modeling method based on GNAT-embedded hybrid snapshot simulation ⋮ Comparing different stabilization strategies for reduced order modeling of viscoelastic fluid flow problems ⋮ Adaptive sparse interpolation for accelerating nonlinear stochastic reduced-order modeling with time-dependent bases ⋮ Adaptive symplectic model order reduction of parametric particle-based Vlasov–Poisson equation ⋮ POD-based model order reduction with an adaptive snapshot selection for a discontinuous Galerkin approximation of the time-domain Maxwell's equations ⋮ Structure-preserving model reduction of second-order systems by Krylov subspace methods

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