Pages that link to "Item:Q2192840"

The following pages link to Convolutional neural networks (CNN) for feature-based model calibration under uncertain geologic scenarios (Q2192840):

Displaying 9 items.

• A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems (Q776737) (← links)
• Deep-learning-based surrogate flow modeling and geological parameterization for data assimilation in 3D subsurface flow (Q2021999) (← links)
• Conditioning generative adversarial networks on nonlinear data for subsurface flow model calibration and uncertainty quantification (Q2130944) (← links)
• Latent-space inversion (LSI): a deep learning framework for inverse mapping of subsurface flow data (Q2130947) (← links)
• A heteroencoder architecture for prediction of failure locations in porous metals using variational inference (Q2160437) (← links)
• The use of convolutional neural networks to search for fractures in the geological media (Q6043913) (← links)
• Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator: concept for bias correction, assessment of surrogate reliability and its application to the carbon dioxide benchmark (Q6074252) (← links)
• Deep learning-aided image-oriented history matching of geophysical data (Q6074265) (← links)
• Convolutional -- recurrent neural network proxy for robust optimization and closed-loop reservoir management (Q6106104) (← links)