13 citations · 13 across the 1 of their papers we have counts for
5 papers
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…
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…
A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems
Meng Tang, Yimin Liu, Louis J. Durlofsky
A deep-learning-based surrogate model is developed and applied for predicting dynamic subsurface flow in channelized geological models. The surrogate model is based on deep convolu…
Deep-learning-based reduced-order modeling for subsurface flow simulation
Zhaoyang Larry Jin, Yimin Liu, Louis J. Durlofsky
A new deep-learning-based reduced-order modeling (ROM) framework is proposed for application in subsurface flow simulation. The reduced-order model is based on an existing embed-to…
A Deep-Learning-Based Geological Parameterization for History Matching Complex Models
Yimin Liu, Wenyue Sun, Louis J. Durlofsky
A new low-dimensional parameterization based on principal component analysis (PCA) and convolutional neural networks (CNN) is developed to represent complex geological models. The…