Pages that link to "Item:Q2289636"
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The following pages link to A deep learning approach for efficiently and accurately evaluating the flow field of supercritical airfoils (Q2289636):
Displaying 12 items.
- Towards high-accuracy deep learning inference of compressible flows over aerofoils (Q2108599) (← links)
- Mesh-Conv: convolution operator with mesh resolution independence for flow field modeling (Q2133572) (← links)
- High Reynolds number airfoil turbulence modeling method based on machine learning technique (Q2670056) (← 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)
- Training a Neural-Network-Based Surrogate Model for Aerodynamic Optimisation Using a Gaussian Process (Q5880409) (← 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 reinforcement learning-based active control for drag reduction of three equilateral-triangular circular cylinders (Q6548366) (← links)
- Complex nonlinear dynamics and vibration suppression of conceptual airfoil models: a state-of-the-art overview (Q6565143) (← links)
