Approximation of skewed interfaces with tensor-based model reduction procedures: application to the reduced basis hierarchical model reduction approach

DOI10.1016/j.jcp.2016.06.021zbMath1349.74007arXiv1406.7426OpenAlexW312756907MaRDI QIDQ727030

Mario Ohlberger, Kathrin Smetana

Publication date: 5 December 2016

Published in: SIAM Journal on Scientific Computing, Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1406.7426

zbMATH Keywords

convergencefinite elementsproper orthogonal decompositiondimensional reductionnumerical experimentcomputational efficiencyadaptive modelingadvection-diffusion equationa posteriori error estimationadaptive refinementproper generalized decompositionreduced basis methodshierarchical model reductiontensor-based model reduction

Mathematics Subject Classification ID

Boundary value problems for second-order elliptic equations (35J25) Error bounds for boundary value problems involving PDEs (65N15) Finite element, Rayleigh-Ritz and Galerkin methods for boundary value problems involving PDEs (65N30) Mesh generation, refinement, and adaptive methods for boundary value problems involving PDEs (65N50) Complexity and performance of numerical algorithms (65Y20) Software, source code, etc. for problems pertaining to mechanics of deformable solids (74-04)

Related Items (7)

HiMod reduction of advection-diffusion-reaction problems with general boundary conditions ⋮ Hierarchical model reduction driven by a proper orthogonal decomposition for parametrized advection-diffusion-reaction problems ⋮ Hierarchical model reduction driven by machine learning for parametric advection-diffusion-reaction problems in the presence of noisy data ⋮ Coupled model and grid adaptivity in hierarchical reduction of elliptic problems ⋮ Approximation of skewed interfaces with tensor-based model reduction procedures: application to the reduced basis hierarchical model reduction approach ⋮ An Uncertainty-Weighted Asynchronous ADMM Method for Parallel PDE Parameter Estimation ⋮ Low-CP-rank tensor completion via practical regularization

Uses Software

• rbMIT

Cites Work

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