Pages that link to "Item:Q2333058"

The following pages link to The use of the Reynolds force vector in a physics informed machine learning approach for predictive turbulence modeling (Q2333058):

Displaying 14 items.

• RANS turbulence model development using CFD-driven machine learning (Q777616) (← links)
• Deep learning of the spanwise-averaged Navier-Stokes equations (Q2123996) (← links)
• A turbulent eddy-viscosity surrogate modeling framework for Reynolds-averaged Navier-Stokes simulations (Q2245422) (← links)
• High Reynolds number airfoil turbulence modeling method based on machine learning technique (Q2670056) (← links)
• Conditioning and accurate solutions of Reynolds average Navier–Stokes equations with data-driven turbulence closures (Q3388856) (← links)
• Ensemble Kalman method for learning turbulence models from indirect observation data (Q5038553) (← links)
• Reynolds averaged turbulence modelling using deep neural networks with embedded invariance (Q5360504) (← links)
• On the Generalizability of Machine-Learning-Assisted Anisotropy Mappings for Predictive Turbulence Modelling (Q5880411) (← links)
• Physics-informed machine learning for surrogate modeling of wind pressure and optimization of pressure sensor placement (Q6044216) (← links)
• Comparison of different data-assimilation approaches to augment RANS turbulence models (Q6060768) (← links)
• Feature importance in neural networks as a means of interpretation for data-driven turbulence models (Q6095918) (← links)
• A highly accurate strategy for data-driven turbulence modeling (Q6125393) (← links)
• Three dimensional interface normal prediction for volume-of-fluid method using artificial neural network (Q6572762) (← links)
• A data-driven turbulence modeling for the Reynolds stress tensor transport equation (Q6659842) (← links)