Pages that link to "Item:Q4997904"
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The following pages link to Numerical investigation of minimum drag profiles in laminar flow using deep learning surrogates (Q4997904):
Displaying 7 items.
- A supervised neural network for drag prediction of arbitrary 2D shapes in laminar flows at low Reynolds number (Q2019945) (← links)
- Multi-fidelity deep neural network surrogate model for aerodynamic shape optimization (Q2020786) (← links)
- Towards high-accuracy deep learning inference of compressible flows over aerofoils (Q2108599) (← links)
- A deep learning approach for efficiently and accurately evaluating the flow field of supercritical airfoils (Q2289636) (← 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)
- Deep learning in computational mechanics: a review (Q6604128) (← links)
