Pages that link to "Item:Q2106998"
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The following pages link to Theory-guided physics-informed neural networks for boundary layer problems with singular perturbation (Q2106998):
Displaying 9 items.
- Convergence of Physics-Informed Neural Networks Applied to Linear Second-Order Elliptic Interface Problems (Q5887902) (← links)
- The robust physics-informed neural networks for a typical fourth-order phase field model (Q6103706) (← links)
- Variable linear transformation improved physics-informed neural networks to solve thin-layer flow problems (Q6119307) (← links)
- Automatic boundary fitting framework of boundary dependent physics-informed neural network solving partial differential equation with complex boundary conditions (Q6171169) (← links)
- Less Emphasis on Hard Regions: Curriculum Learning of PINNs for Singularly Perturbed Convection-Diffusion-Reaction Problems (Q6192635) (← links)
- Semi-analytic PINN methods for boundary layer problems in a rectangular domain (Q6581956) (← links)
- Multistep asymptotic pre-training strategy based on PINNs for solving steep boundary singular perturbation problems (Q6609750) (← links)
- Approximation and generalization of DeepONets for learning operators arising from a class of singularly perturbed problems (Q6630935) (← links)
- Efficiently training physics-informed neural networks via anomaly-aware optimization (Q6662394) (← links)
