Implementation and detailed assessment of a GNAT reduced-order model for subsurface flow simulation

DOI10.1016/j.jcp.2018.11.038OpenAlexW2904704941WikidataQ128754325 ScholiaQ128754325MaRDI QIDQ2169505

Louis J. Durlofsky, Rui Jiang

Publication date: 2 September 2022

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

Full work available at URL: https://doi.org/10.1016/j.jcp.2018.11.038

zbMATH Keywords

proper orthogonal decompositionreservoir simulationreduced-order modelingdiscrete empirical interpolation methodtrajectory piecewise linearizationGauss-Newton with approximated tensors

Mathematics Subject Classification ID

Basic methods in fluid mechanics (76Mxx) Geophysics (86Axx) Flows in porous media; filtration; seepage (76Sxx)

Related Items (6)

A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder ⋮ A reduced order modeling method based on GNAT-embedded hybrid snapshot simulation ⋮ Component-wise reduced order model lattice-type structure design ⋮ Reduced order models for Lagrangian hydrodynamics ⋮ Adaptive POD-Galerkin technique for reservoir simulation and optimization ⋮ Local Lagrangian reduced-order modeling for the Rayleigh-Taylor instability by solution manifold decomposition

Cites Work

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• Application of POD and DEIM on dimension reduction of non-linear miscible viscous fingering in porous media
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