Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network
type: Item
instance of: scholarly article
title: Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network
author: Nanzhe Wang
author: Haibin Chang
author: Dongxiao Zhang
published in: Journal of Computational Physics
publication date: 2022-07-21T00:00:00Z
full work available at URL: https://arxiv.org/abs/2110.10080
Mathematics Subject Classification ID: 68Txx
Mathematics Subject Classification ID: 35Rxx
Mathematics Subject Classification ID: 65Cxx
zbMATH DE Number: 7561094
zbMATH Keywords: theory-guided convolutional neural network
zbMATH Keywords: surrogate modeling
zbMATH Keywords: inverse modeling
zbMATH Keywords: two-phase flow
Wikidata QID: Q114163255
MaRDI profile type: Publication
OpenAlex ID: W3207319801
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DOI: 10.1016/J.JCP.2022.111419
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description: scientific article