13 citations · 20 across the 7 of their papers we have counts for
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physics.comp-ph2020
Deep-learning-based surrogate flow modeling and geological parameterization for data assimilation in 3D subsurface flow
Meng Tang, Yimin Liu, Louis J. Durlofsky
Data assimilation in subsurface flow systems is challenging due to the large number of flow simulations often required, and by the need to preserve geological realism in the calibr…
cs.CV2020
3D CNN-PCA: A Deep-Learning-Based Parameterization for Complex Geomodels
Yimin Liu, Louis J. Durlofsky
Geological parameterization enables the representation of geomodels in terms of a relatively small set of variables. Parameterization is therefore very useful in the context of dat…
stat.ML2020★ 4 cited
Data-Space Inversion Using a Recurrent Autoencoder for Time-Series Parameterization
Su Jiang, Louis J. Durlofsky
Data-space inversion (DSI) and related procedures represent a family of methods applicable for data assimilation in subsurface flow settings. These methods differ from model-based…