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Found 9 papers, 0 papers with code
Uncertainty quantification of two-phase flow in porous media via coupled-TgNN surrogate model
In this work, a novel coupled theory-guided neural network (TgNN) based surrogate model is built to facilitate computation efficiency under the premise of satisfactory accuracy.
Inferring electrochemical performance and parameters of Li-ion batteries based on deep operator networks
To realize accuracy and efficiency simultaneously in battery modeling, we propose to build a data-driven surrogate for a battery system while incorporating the underlying physics as constraints.
RockGPT: Reconstructing three-dimensional digital rocks from single two-dimensional slice from the perspective of video generation
In order to obtain diverse reconstructions, the discrete latent codes are modeled using conditional GPT in an autoregressive manner, while incorporating conditional information from a given slice, rock type, and porosity.
Digital rock reconstruction with user-defined properties using conditional generative adversarial networks
In fact, the proposed framework can realize the targets of MPS and TPS simultaneously by incorporating high-order information directly from rock images with the GANs scheme, while preserving low-order counterparts through conditioning.
Using Deep Learning to Improve Ensemble Smoother: Applications to Subsurface Characterization
In this new update scheme, a high volume of training data are generated from a relatively small-sized ensemble of model parameters and simulation outputs, and possible non-Gaussian features can be preserved in the training data and captured by an adequate DL model.
Physics-informed semantic inpainting: Application to geostatistical modeling
A fundamental problem in geostatistical modeling is to infer the heterogeneous geological field based on limited measurements and some prior spatial statistics.
Transfer learning for diagnosis of congenital abnormalities of the kidney and urinary tract in children based on Ultrasound imaging data
In this study, we propose a transfer learning-based method to extract imaging features from US kidney images in order to improve the CAKUT diagnosis in children.
Integrating semi-supervised label propagation and random forests for multi-atlas based hippocampus segmentation
The semi-supervised label propagation method takes into consideration local and global image appearance of images to be segmented and segments the images by propagating reliable segmentation results obtained by the supervised random forests method.
A dynamic graph-cuts method with integrated multiple feature maps for segmenting kidneys in ultrasound images
The proposed method has been evaluated and compared with state of the art image segmentation methods based on clinical kidney US images of 85 subjects.
