Pages that link to "Item:Q6111307"
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The following pages link to Investigating and Mitigating Failure Modes in Physics-Informed Neural Networks (PINNs) (Q6111307):
Displaying 12 items.
- Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks (Q2072449) (← links)
- A novel sequential method to train physics informed neural networks for Allen Cahn and Cahn Hilliard equations (Q2072734) (← links)
- Physics-informed graph neural Galerkin networks: a unified framework for solving PDE-governed forward and inverse problems (Q2072742) (← links)
- Learning by neural networks under physical constraints for simulation in fluid mechanics (Q2101998) (← links)
- Physics-informed neural networks for gravity field modeling of small bodies (Q2104214) (← links)
- Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems (Q2138842) (← links)
- Residual-based adaptivity for two-phase flow simulation in porous media using physics-informed neural networks (Q2156788) (← links)
- On the eigenvector bias of Fourier feature networks: from regression to solving multi-scale PDEs with physics-informed neural networks (Q2237440) (← links)
- Control of partial differential equations via physics-informed neural networks (Q2696946) (← links)
- Bi-Orthogonal fPINN: A Physics-Informed Neural Network Method for Solving Time-Dependent Stochastic Fractional PDEs (Q6143622) (← links)
- Splitting physics-informed neural networks for inferring the dynamics of integer- and fractional-order neuron models (Q6537067) (← links)
- Efficiently training physics-informed neural networks via anomaly-aware optimization (Q6662394) (← links)
