Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network

DOI10.1016/j.jcp.2022.111419OpenAlexW3207319801WikidataQ114163255 ScholiaQ114163255MaRDI QIDQ2157149

Dongxiao Zhang, Nanzhe Wang, Haibin Chang

Publication date: 21 July 2022

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

Full work available at URL: https://arxiv.org/abs/2110.10080

zbMATH Keywords

two-phase flowinverse modelingsurrogate modelingtheory-guided convolutional neural network

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Miscellaneous topics in partial differential equations (35Rxx) Probabilistic methods, stochastic differential equations (65Cxx)

Related Items (1)

A physics-informed convolutional neural network for the simulation and prediction of two-phase Darcy flows in heterogeneous porous media

Cites Work

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