Interpolation-based reduced-order modelling for steady transonic flows via manifold learning

DOI10.1080/10618562.2014.918695OpenAlexW2070260349MaRDI QIDQ5071728

No author found.

Publication date: 22 April 2022

Published in: International Journal of Computational Fluid Dynamics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10618562.2014.918695

zbMATH Keywords

proper orthogonal decompositiondimensionality reductionaerodynamicsmanifold learningreduced-order modelisomap

Mathematics Subject Classification ID

Fluid mechanics (76-XX) Numerical analysis (65-XX)

Related Items

Registration-based model reduction of parameterized two-dimensional conservation laws ⋮ Unsteady physics-based reduced order modeling for large-scale compressible aerodynamic applications ⋮ A stabilized POD model for turbulent flows over a range of Reynolds numbers: optimal parameter sampling and constrained projection ⋮ Non-linear manifold reduced-order models with convolutional autoencoders and reduced over-collocation method ⋮ Adaptive Sampling for Nonlinear Dimensionality Reduction Based on Manifold Learning ⋮ On the comparison of LES data-driven reduced order approaches for hydroacoustic analysis ⋮ On a minimum enclosing ball of a collection of linear subspaces ⋮ 7 Manifold interpolation ⋮ Adaptive reduced basis method for the reconstruction of unsteady vortex-dominated flows ⋮ 6 Model reduction in computational aerodynamics ⋮ From snapshots to manifolds – a tale of shear flows

Uses Software

• TAU
• Isomap
• Spalart-Allmaras

Cites Work

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• 10.1162/153244304322972667
• Turbulence, Coherent Structures, Dynamical Systems and Symmetry
• An "$hp$" Certified Reduced Basis Method for Parametrized Elliptic Partial Differential Equations
• Localized Discrete Empirical Interpolation Method
• Modeling and control of physical processes using proper orthogonal decomposition