Pages that link to "Item:Q2021999"

The following pages link to Deep-learning-based surrogate flow modeling and geological parameterization for data assimilation in 3D subsurface flow (Q2021999):

Displaying 11 items.

• Efficient uncertainty quantification for dynamic subsurface flow with surrogate by theory-guided neural network (Q2020800) (← links)
• Use of multifidelity training data and transfer learning for efficient construction of subsurface flow surrogate models (Q2112502) (← links)
• Weak form theory-guided neural network (TgNN-wf) for deep learning of subsurface single- and two-phase flow (Q2131089) (← links)
• A data-driven approach to full-field nonlinear stress distribution and failure pattern prediction in composites using deep learning (Q2145129) (← links)
• Convolutional -- recurrent neural network proxy for robust optimization and closed-loop reservoir management (Q6106104) (← links)
• Dual order-reduced Gaussian process emulators (DORGP) for quantifying high-dimensional uncertain crack growth using limited and noisy data (Q6194158) (← links)
• Graph network surrogate model for subsurface flow optimization (Q6560712) (← links)
• Stochastic pix2vid: a new spatiotemporal deep learning method for image-to-video synthesis in geologic \(\mathrm{CO_2}\) storage prediction (Q6600805) (← links)
• Modelling of compressible multi-component two-phase flow with multi-component Navier boundary condition (Q6646468) (← links)
• Physics-informed multi-grid neural operator: theory and an application to porous flow simulation (Q6648363) (← links)
• An efficient computational framework for data assimilation of fractional-order dynamical system with sparse observations (Q6663399) (← links)