A data-driven approach for a macroscopic conductivity model utilizing finite element approximation

DOI10.1016/j.jcp.2022.111394OpenAlexW4283208535WikidataQ114163266 ScholiaQ114163266MaRDI QIDQ2157113

Hee Jun Yang, Young Jae Jeon, Hyea Hyun Kim

Publication date: 21 July 2022

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

Full work available at URL: https://doi.org/10.1016/j.jcp.2022.111394

zbMATH Keywords

neural networkoptimal parametermultiscalemortar methodmacroscopic conductivity

Mathematics Subject Classification ID

Numerical linear algebra (65Fxx) Numerical and other methods in solid mechanics (74Sxx) Numerical methods for partial differential equations, boundary value problems (65Nxx)

Cites Work

• The local microscale problem in the multiscale modeling of strongly heterogeneous media: Effects of boundary conditions and cell size
• A multiscale finite element method for elliptic problems in composite materials and porous media
• Analysis of upscaling absolute permeability
• Mortar upscaling for multiphase flow in porous media
• Domain Decomposition Preconditioners for Multiscale Flows in High-Contrast Media
• A Simplified Method for Upscaling Composite Materials with High Contrast of the Conductivity
• A Mortar Finite Element Method Using Dual Spaces for the Lagrange Multiplier
• A BDDC Algorithm with Enriched Coarse Spaces for Two-Dimensional Elliptic Problems with Oscillatory and High Contrast Coefficients
• The Mathematical Theory of Finite Element Methods
• Finite Elements
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