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Verfasst von:Kreyenberg, Philipp J. [VerfasserIn]   i
 Bauser, Hannes [VerfasserIn]   i
 Roth, Kurt [VerfasserIn]   i
Titel:Velocity field estimation on density-driven solute transport with a convolutional neural network
Verf.angabe:Philipp J. Kreyenberg, Hannes H. Bauser, and Kurt Roth
E-Jahr:2019
Jahr:29 Aug 2019
Umfang:19 S.
Illustrationen:Illustrationen
Fussnoten:Gesehen am 02.01.2019
Titel Quelle:Enthalten in: Water resources research
Ort Quelle:[New York] : Wiley, 1965
Jahr Quelle:2019
Band/Heft Quelle:55(2019), 8, Seite 7275-7293
ISSN Quelle:1944-7973
Abstract:Recent advances in machine learning open new opportunities to gain deeper insight into hydrological systems, where some relevant system quantities remain difficult to measure. We use deep learning methods trained on numerical simulations of the physical processes to explore the possibilities of closing the information gap of missing system quantities. As an illustrative example we study the estimation of velocity fields in numerical and laboratory experiments of density-driven solute transport. Using high-resolution observations of the solute concentration distribution, we demonstrate the capability of the method to structurally incorporate the representation of the physical processes. Velocity field estimation for synthetic data for both variable and uniform concentration boundary conditions showed equal results. This capability is remarkable because only the latter was employed for training the network. Applying the method to measured concentration distributions of density-driven solute transport in a Hele-Shaw cell makes the velocity field assessable in the experiment. This assessability of the velocity field even holds for regions with negligible solute concentration between the density fingers, where the velocity field is otherwise inaccessible.
DOI:doi:10.1029/2019WR024833
URL:Volltext ; Verlag: https://doi.org/10.1029/2019WR024833
 Volltext: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019WR024833
 DOI: https://doi.org/10.1029/2019WR024833
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:convolutional neural network
 density-driven active solute transport
 Hele-Shaw cell experiment
 velocity field estimation
K10plus-PPN:1686376871
Verknüpfungen:→ Zeitschrift
 
 
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