Pages that link to "Item:Q2187913"
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The following pages link to Deep global model reduction learning in porous media flow simulation (Q2187913):
Displaying 16 items.
- Mode decomposition methods for flows in high-contrast porous media. A global approach (Q348464) (← links)
- A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems (Q776737) (← links)
- A multi-stage deep learning based algorithm for multiscale model reduction (Q2029406) (← links)
- Data-driven reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximation (Q2059122) (← links)
- Bayesian sparse learning with preconditioned stochastic gradient MCMC and its applications (Q2128484) (← links)
- Learning rays via deep neural network in a ray-based IPDG method for high-frequency Helmholtz equations in inhomogeneous media (Q2157085) (← links)
- A deep learning based reduced order modeling for stochastic underground flow problems (Q2162031) (← links)
- A data-driven surrogate to image-based flow simulations in porous media (Q2176870) (← links)
- Reduced-order deep learning for flow dynamics. The interplay between deep learning and model reduction (Q2222675) (← links)
- A deep learning based nonlinear upscaling method for transport equations (Q2672199) (← links)
- AMS-Net: Adaptive Multiscale Sparse Neural Network with Interpretable Basis Expansion for Multiphase Flow Problems (Q5099843) (← links)
- Physics-informed data-driven model for fluid flow in porous media (Q6093463) (← links)
- Prediction of numerical homogenization using deep learning for the Richards equation (Q6098948) (← links)
- Learning computational upscaling models for a class of convection-diffusion equations (Q6556746) (← links)
- Learning-based multi-continuum model for multiscale flow problems (Q6589888) (← links)
- Prediction of discretization of online GMsFEM using deep learning for Richards equation (Q6593325) (← links)
