Pages that link to "Item:Q6092912"
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The following pages link to Fluid Flow Modelling Using Physics-Informed Convolutional Neural Network in Parametrised Domains (Q6092912):
Displaying 6 items.
- NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations (Q2127017) (← links)
- Weak form theory-guided neural network (TgNN-wf) for deep learning of subsurface single- and two-phase flow (Q2131089) (← links)
- Deep-learning accelerated calculation of real-fluid properties in numerical simulation of complex flowfields (Q2132659) (← links)
- Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network (Q2157149) (← links)
- Physics-informed neural networks for high-speed flows (Q2175317) (← links)
- Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (Q2176917) (← links)
