Pages that link to "Item:Q2019945"
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The following pages link to A supervised neural network for drag prediction of arbitrary 2D shapes in laminar flows at low Reynolds number (Q2019945):
Displaying 8 items.
- Derivation of a correlation for drag coefficient in two-dimensional bounded supercavitating flows, using artificial neural networks (Q363135) (← links)
- Development of an algorithm for reconstruction of droplet history based on deposition pattern using computational fluid dynamics and convolutional neural network (Q2021070) (← links)
- Bridging the gap: machine learning to resolve improperly modeled dynamics (Q2116291) (← links)
- An application of neural networks to the prediction of aerodynamic coefficients of aerofoils and wings (Q2243469) (← links)
- Prediction of aerodynamic flow fields using convolutional neural networks (Q2319410) (← links)
- A deep learning approach for the transonic flow field predictions around airfoils (Q2670066) (← links)
- Surrogate convolutional neural network models for steady computational fluid dynamics simulations (Q2672202) (← links)
- Numerical investigation of minimum drag profiles in laminar flow using deep learning surrogates (Q4997904) (← links)
