Pages that link to "Item:Q2670066"
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The following pages link to A deep learning approach for the transonic flow field predictions around airfoils (Q2670066):
Displaying 11 items.
- A data assimilation methodology for reconstructing turbulent flows around aircraft (Q728958) (← links)
- Three-dimensional realizations of flood flow in large-scale rivers using the neural fuzzy-based machine-learning algorithms (Q2084088) (← links)
- Learning by neural networks under physical constraints for simulation in fluid mechanics (Q2101998) (← links)
- Towards high-accuracy deep learning inference of compressible flows over aerofoils (Q2108599) (← 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)
- High Reynolds number airfoil turbulence modeling method based on machine learning technique (Q2670056) (← links)
- A comparative study of learning techniques for the compressible aerodynamics over a transonic RAE2822 airfoil (Q2698730) (← links)
- Data-Driven, Physics-Based Feature Extraction from Fluid Flow Fields using Convolutional Neural Networks (Q5161377) (← links)
- Physics-informed deep learning for simultaneous surrogate modeling and PDE-constrained optimization of an airfoil geometry (Q6097587) (← links)
