Upscaling of two-phase discrete fracture simulations using a convolutional neural network
type: Item
instance of: scholarly article
title: Upscaling of two-phase discrete fracture simulations using a convolutional neural network
zbMATH Open document ID: 1496.86002
author: Nikolai Andrianov
published in: Computational Geosciences
publication date: 2022-10-14T00:00:00Z
Mathematics Subject Classification ID: 86-08
Mathematics Subject Classification ID: 76S05
Mathematics Subject Classification ID: 76M12
Mathematics Subject Classification ID: 76M50
Mathematics Subject Classification ID: 76M55
Mathematics Subject Classification ID: 68T07
Mathematics Subject Classification ID: 65M60
Mathematics Subject Classification ID: 74-10
Mathematics Subject Classification ID: 74R10
Mathematics Subject Classification ID: 74S05
zbMATH DE Number: 7601657
zbMATH Keywords: machine learning
zbMATH Keywords: discrete fracture-matrix (DFM) modelling
zbMATH Keywords: convolutional neural network
zbMATH Keywords: fractured reservoirs
describes a project that uses: Keras
describes a project that uses: DUNE-ALUGrid
describes a project that uses: DuMuX
describes a project that uses: ALUGrid
describes a project that uses: Gmsh
describes a project that uses: Matlab
describes a project that uses: Adam
describes a project that uses: dfnWorks
describes a project that uses: MRST
describes a project that uses: GitHub
MaRDI profile type: Publication
full work available at URL: s10596-022-10149-3
OpenAlex ID: W4281769109
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cites work: An efficient multi-point flux approximation method for discrete fracture-matrix simulations
cites work: Q2934059
cites work: Use of border regions for improved permeability upscaling
DOI: 10.1007/S10596-022-10149-3
description: scientific article; zbMATH DE number 7601657