Pages that link to "Item:Q2162011"
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The following pages link to Physics-informed neural networks for learning the homogenized coefficients of multiscale elliptic equations (Q2162011):
Displaying 13 items.
- Learning phase field mean curvature flows with neural networks (Q2083658) (← links)
- GINNs: graph-informed neural networks for multiscale physics (Q2120776) (← links)
- Parallel physics-informed neural networks via domain decomposition (Q2133497) (← links)
- A Rate of Convergence of Physics Informed Neural Networks for the Linear Second Order Elliptic PDEs (Q5077701) (← links)
- Convergence of Physics-Informed Neural Networks Applied to Linear Second-Order Elliptic Interface Problems (Q5887902) (← links)
- Prediction of numerical homogenization using deep learning for the Richards equation (Q6098948) (← links)
- The robust physics-informed neural networks for a typical fourth-order phase field model (Q6103706) (← links)
- Multi-fidelity physics constrained neural networks for dynamical systems (Q6153908) (← links)
- Asymptotic-preserving neural networks for multiscale time-dependent linear transport equations (Q6158979) (← links)
- A Neural Network Approach for Homogenization of Multiscale Problems (Q6178099) (← links)
- Physics-informed neural networks for learning the homogenized coefficients of multiscale elliptic equations (Q6391602) (← links)
- Phase field smoothing-PINN: a neural network solver for partial differential equations with discontinuous coefficients (Q6590262) (← links)
- Prediction of discretization of online GMsFEM using deep learning for Richards equation (Q6593325) (← links)
