Pages that link to "Item:Q2130944"

The following pages link to Conditioning generative adversarial networks on nonlinear data for subsurface flow model calibration and uncertainty quantification (Q2130944):

Displaying 12 items.

• A new approach for conditioning process-based geologic models to well data (Q1789191) (← links)
• Parametric generation of conditional geological realizations using generative neural networks (Q2009823) (← links)
• Efficient uncertainty quantification for dynamic subsurface flow with surrogate by theory-guided neural network (Q2020800) (← links)
• Deep-learning-based surrogate flow modeling and geological parameterization for data assimilation in 3D subsurface flow (Q2021999) (← links)
• Geological facies modeling based on progressive growing of generative adversarial networks (GANs) (Q2027203) (← links)
• GANSim: conditional facies simulation using an improved progressive growing of generative adversarial networks (GANs) (Q2066806) (← links)
• Use of multifidelity training data and transfer learning for efficient construction of subsurface flow surrogate models (Q2112502) (← links)
• Latent-space inversion (LSI): a deep learning framework for inverse mapping of subsurface flow data (Q2130947) (← links)
• Convolutional neural networks (CNN) for feature-based model calibration under uncertain geologic scenarios (Q2192840) (← links)
• U-net generative adversarial network for subsurface facies modeling (Q2225390) (← links)
• Application of Bayesian generative adversarial networks to geological facies modeling (Q2676501) (← links)
• Graph network surrogate model for subsurface flow optimization (Q6560712) (← links)