Pages that link to "Item:Q2134766"
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The following pages link to Multifidelity modeling for physics-informed neural networks (PINNs) (Q2134766):
Displaying 9 items.
- GINNs: graph-informed neural networks for multiscale physics (Q2120776) (← links)
- Physics-informed multi-LSTM networks for metamodeling of nonlinear structures (Q2236167) (← links)
- Improved deep neural networks with domain decomposition in solving partial differential equations (Q2674166) (← links)
- A metalearning approach for physics-informed neural networks (PINNs): application to parameterized PDEs (Q2681136) (← links)
- Physics-informed neural networks for data-driven simulation: advantages, limitations, and opportunities (Q2683126) (← links)
- fPINNs: Fractional Physics-Informed Neural Networks (Q5230662) (← links)
- Multifidelity deep operator networks for data-driven and physics-informed problems (Q6048427) (← links)
- A unified scalable framework for causal sweeping strategies for physics-informed neural networks (PINNs) and their temporal decompositions (Q6048429) (← links)
- An Adaptive Non-Intrusive Multi-Fidelity Reduced Basis Method for Parameterized Partial Differential Equations (Q6110109) (← links)
