Pages that link to "Item:Q6068270"

The following pages link to A perspective on machine learning methods in turbulence modeling (Q6068270):

Displaying 13 items.

• RANS turbulence model development using CFD-driven machine learning (Q777616) (← links)
• Dynamic calibration of differential equations using machine learning, with application to turbulence models (Q2135788) (← links)
• The use of the Reynolds force vector in a physics informed machine learning approach for predictive turbulence modeling (Q2333058) (← links)
• Machine learning in sedimentation modelling. (Q2490829) (← links)
• High Reynolds number airfoil turbulence modeling method based on machine learning technique (Q2670056) (← links)
• Ensemble Gradient for Learning Turbulence Models from Indirect Observations (Q5065144) (← links)
• Physics-informed machine learning for surrogate modeling of wind pressure and optimization of pressure sensor placement (Q6044216) (← links)
• Combining direct and indirect sparse data for learning generalizable turbulence models (Q6107115) (← links)
• (Q6161794) (← links)
• Revisiting tensor basis neural network for Reynolds stress modeling: application to plane channel and square duct flows (Q6566970) (← links)
• Machine learning-based WENO5 scheme (Q6585341) (← links)
• Enhancing CFD Solver with machine learning techniques (Q6588283) (← links)
• Differentiability in unrolled training of neural physics simulators on transient dynamics (Q6663245) (← links)