An innovative application of deep learning in multiscale modeling of subsurface fluid flow Reconstructing the basis functions of the mixed Generalized Multiscale Finite Element Method (GMsFEM)

Coenen, Frans ORCID: 0000-0003-1026-6649, Choubineh, Abouzar, Chen, Jie and Ma, Fei (2022) An innovative application of deep learning in multiscale modeling of subsurface fluid flow Reconstructing the basis functions of the mixed Generalized Multiscale Finite Element Method (GMsFEM). Journal of Petroleum Science and Engineering.

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Item Type:	Article
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	08 Jul 2022 12:46
Last Modified:	18 Jan 2023 20:56
Open Access URL:	https://www.sciencedirect.com/science/article/pii/...
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3158022

Available Versions of this Item

• An innovative application of deep learning in multiscale modeling of subsurface fluid flow Reconstructing the basis functions of the mixed Generalized Multiscale Finite Element Method (GMsFEM). (deposited 08 Jul 2022 12:46) [Currently Displayed]
  • An innovative application of deep learning in multiscale modeling of subsurface fluid flow: Reconstructing the basis functions of the mixed GMsFEM. (deposited 25 Jul 2022 10:18)