Pages that link to "Item:Q6098948"
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The following pages link to Prediction of numerical homogenization using deep learning for the Richards equation (Q6098948):
Displaying 11 items.
- Learning macroscopic parameters in nonlinear multiscale simulations using nonlocal multicontinua upscaling techniques (Q776681) (← links)
- A deep learning based nonlinear upscaling method for transport equations (Q2672199) (← links)
- Constraint energy minimizing generalized multiscale finite element method for multi-continuum Richards equations (Q2681139) (← links)
- Neural Network Approximation of Coarse-Scale Surrogates in Numerical Homogenization (Q6066782) (← links)
- A computational macroscopic model of piezomagnetoelectric materials using generalized multiscale finite element method (Q6073131) (← links)
- Non-local multi-continuum method (NLMC) for Darcy-Forchheimer flow in fractured media (Q6079944) (← links)
- Multicontinuum homogenization for Richards' equation: the derivation and numerical experiments (Q6116761) (← links)
- Advancing wave equation analysis in dual-continuum systems: a partial learning approach with discrete empirical interpolation and deep neural networks (Q6489267) (← links)
- Prediction of discretization of online GMsFEM using deep learning for Richards equation (Q6593325) (← links)
- Generalized multiscale finite element method for a nonlinear elastic strain-limiting Cosserat model (Q6639330) (← links)
- Deep learning surrogate for predicting hydraulic conductivity tensors from stochastic discrete fracture-matrix models (Q6662489) (← links)
