Pages that link to "Item:Q2681136"
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The following pages link to A metalearning approach for physics-informed neural networks (PINNs): application to parameterized PDEs (Q2681136):
Displaying 7 items.
- Meta-mgnet: meta multigrid networks for solving parameterized partial differential equations (Q2133752) (← links)
- Physics-informed multi-LSTM networks for metamodeling of nonlinear structures (Q2236167) (← links)
- Multi-Fidelity Machine Learning Applied to Steady Fluid Flows (Q5880416) (← links)
- A unified scalable framework for causal sweeping strategies for physics-informed neural networks (PINNs) and their temporal decompositions (Q6048429) (← links)
- Branched latent neural maps (Q6118560) (← links)
- Solving seepage equation using physics-informed residual network without labeled data (Q6120152) (← links)
- Approximating families of sharp solutions to Fisher's equation with physics-informed neural networks (Q6660246) (← links)
