Geo-guided deep learning for spatial downscaling of solute transport in heterogeneous porous media

Resolving solute transport in heterogeneous porous media is a complex task, because of the sparse experimental data and the high computational cost of numerical simulations. This work proposes a unique two-stage deep learning architecture comprising a dual-branch autoencoder and a geo-guided super-r...

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Veröffentlicht in:	Computers & geosciences Jg. 188; H. C; S. 105599
Hauptverfasser:	Pawar, Nikhil M., Soltanmohammadi, Ramin, Faroughi, Shirko, Faroughi, Salah A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United Kingdom Elsevier Ltd 01.06.2024 Elsevier
Schlagworte:	Autoencoders Data imputation domain finite element analysis Generative adversarial network Geo-guided deep learning Heterogeneous porous media permeability signal-to-noise ratio Solute transport solutes Spatial downscaling Super-resolution Super-resolution Solute transport Data imputation Autoencoders Generative adversarial network Spatial downscaling Heterogeneous porous media Geo-guided deep learning
ISSN:	0098-3004, 1873-7803
Online-Zugang:	Volltext
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