Pages that link to "Item:Q2084593"
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The following pages link to Solving PDEs by variational physics-informed neural networks: an a posteriori error analysis (Q2084593):
Displaying 11 items.
- \textit{hp}-VPINNs: variational physics-informed neural networks with domain decomposition (Q2021230) (← links)
- CENN: conservative energy method based on neural networks with subdomains for solving variational problems involving heterogeneous and complex geometries (Q2083124) (← links)
- A priori and a posteriori error estimates for the deep Ritz method applied to the Laplace and Stokes problem (Q2095152) (← links)
- Designing rotationally invariant neural networks from PDEs and variational methods (Q2168880) (← links)
- A deep Fourier residual method for solving PDEs using neural networks (Q2683430) (← links)
- An overview on deep learning-based approximation methods for partial differential equations (Q2697278) (← links)
- Neural network-based variational methods for solving quadratic porous medium equations in high dimensions (Q2699489) (← links)
- A Rate of Convergence of Physics Informed Neural Networks for the Linear Second Order Elliptic PDEs (Q5077701) (← links)
- Automatic boundary fitting framework of boundary dependent physics-informed neural network solving partial differential equation with complex boundary conditions (Q6171169) (← links)
- Robust variational physics-informed neural networks (Q6497138) (← links)
- Adaptive deep Fourier residual method via overlapping domain decomposition (Q6557761) (← links)
