Parametric nonlinear model reduction using \(K\)-means clustering for miscible flow simulation

DOI10.1155/2020/3904606zbMath1499.76077OpenAlexW3083265514MaRDI QIDQ2217993

Saifon Chaturantabut, Norapon Sukuntee

Publication date: 12 January 2021

Published in: Journal of Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2020/3904606

Mathematics Subject Classification ID

Flows in porous media; filtration; seepage (76S05) Finite difference methods applied to problems in fluid mechanics (76M20) Spectral methods applied to problems in fluid mechanics (76M22)

Related Items (2)

Analysis of an innovative sampling strategy based on k-means clustering algorithm for POD and POD-DEIM reduced order models of a 2-D reaction-diffusion system ⋮ Stabilized model reduction for nonlinear dynamical systems through a contractivity-preserving framework

Cites Work

• Model order reduction for nonlinear Schrödinger equation
• POD/DEIM reduced-order strategies for efficient four dimensional variational data assimilation
• A combination of proper orthogonal decomposition-discrete empirical interpolation method (POD-DEIM) and meshless local RBF-DQ approach for prevention of groundwater contamination
• Investigation of the Oldroyd model as a generalized incompressible Navier-Stokes equation via the interpolating stabilized element free Galerkin technique
• POD-DEIM model order reduction for strain-softening viscoplasticity
• A Posteriori Error Estimation for DEIM Reduced Nonlinear Dynamical Systems
• A State Space Error Estimate for POD-DEIM Nonlinear Model Reduction
• Nonlinear Model Reduction via Discrete Empirical Interpolation
• Application of POD and DEIM on dimension reduction of non-linear miscible viscous fingering in porous media

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