Pages that link to "Item:Q2124408"
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The following pages link to Active training of physics-informed neural networks to aggregate and interpolate parametric solutions to the Navier-Stokes equations (Q2124408):
Displaying 10 items.
- Discretizationnet: a machine-learning based solver for Navier-Stokes equations using finite volume discretization (Q2021855) (← links)
- A physics-informed learning approach to Bernoulli-type free boundary problems (Q2107176) (← links)
- NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations (Q2127017) (← links)
- Scientific machine learning through physics-informed neural networks: where we are and what's next (Q2162315) (← links)
- Improved deep neural networks with domain decomposition in solving partial differential equations (Q2674166) (← links)
- Physics-Driven Learning of the Steady Navier-Stokes Equations using Deep Convolutional Neural Networks (Q5042008) (← links)
- Physics-informed neural networks for the Reynolds-averaged Navier-Stokes modeling of Rayleigh-Taylor turbulent mixing (Q6060732) (← links)
- Error estimates and physics informed augmentation of neural networks for thermally coupled incompressible Navier Stokes equations (Q6109270) (← links)
- Error assessment of an adaptive finite elements -- neural networks method for an elliptic parametric PDE (Q6202970) (← links)
- Machine learning for nonlinear integro-differential equations with degenerate kernel scheme (Q6591000) (← links)
