Pages that link to "Item:Q2020800"

The following pages link to Efficient uncertainty quantification for dynamic subsurface flow with surrogate by theory-guided neural network (Q2020800):

Displaying 12 items.

• 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)
• Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network (Q2157149) (← links)
• A deep learning based reduced order modeling for stochastic underground flow problems (Q2162031) (← links)
• Theory-guided auto-encoder for surrogate construction and inverse modeling (Q2237777) (← links)
• Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons (Q2681129) (← links)
• A physics-informed convolutional neural network for the simulation and prediction of two-phase Darcy flows in heterogeneous porous media (Q2681146) (← links)
• Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios (Q2683433) (← links)
• HRW: Hybrid Residual and Weak Form Loss for Solving Elliptic Interface Problems with Neural Network (Q6151336) (← links)
• A connection element method: both a new computational method and a physical data-driven framework -- take subsurface two-phase flow as an example (Q6158701) (← links)
• Stochastic pix2vid: a new spatiotemporal deep learning method for image-to-video synthesis in geologic \(\mathrm{CO_2}\) storage prediction (Q6600805) (← links)
• Polynomial chaos expansions on principal geodesic Grassmannian submanifolds for surrogate modeling and uncertainty quantification (Q6639348) (← links)