Pages that link to "Item:Q2108599"
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The following pages link to Towards high-accuracy deep learning inference of compressible flows over aerofoils (Q2108599):
Displaying 13 items.
- Deep-learning accelerated calculation of real-fluid properties in numerical simulation of complex flowfields (Q2132659) (← links)
- DeepM\&Mnet for hypersonics: predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators (Q2133505) (← links)
- An application of neural networks to the prediction of aerodynamic coefficients of aerofoils and wings (Q2243469) (← links)
- Output-based adaptive aerodynamic simulations using convolutional neural networks (Q2245362) (← links)
- A deep learning approach for efficiently and accurately evaluating the flow field of supercritical airfoils (Q2289636) (← 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)
- A comparative study of learning techniques for the compressible aerodynamics over a transonic RAE2822 airfoil (Q2698730) (← links)
- Accurate prediction of the particle image velocimetry flow field and rotor thrust using deep learning (Q3390379) (← links)
- Numerical investigation of minimum drag profiles in laminar flow using deep learning surrogates (Q4997904) (← links)
- Physics-informed deep learning for simultaneous surrogate modeling and PDE-constrained optimization of an airfoil geometry (Q6097587) (← links)
- Airfoil-based convolutional autoencoder and long short-term memory neural network for predicting coherent structures evolution around an airfoil (Q6100106) (← links)
- Deep learning in computational mechanics: a review (Q6604128) (← links)
