102 citations · 130 across the 4 of their papers we have counts for
4 papers · 1 filter
Physical interpretation of neural network-based nonlinear eddy viscosity models
Xin-Lei Zhang, Heng Xiao, Solkeun Jee +1
Neural network-based turbulence modeling has gained significant success in improving turbulence predictions by incorporating high--fidelity data. However, the interpretability of t…
Combining direct and indirect sparse data for learning generalizable turbulence models
Xin-Lei Zhang, Heng Xiao, Xiaodong Luo +1
Learning turbulence models from observation data is of significant interest in discovering a unified model for a broad range of practical flow applications. Either the direct obser…
Learning neural-network-based turbulence models for external transonic flows using ensemble Kalman method
Yi Liu, Xin-Lei Zhang, Guowei He
This paper presents a neural network-based turbulence modeling approach for transonic flows based on the ensemble Kalman method. The approach adopts a tensor basis neural network f…
Ensemble Kalman method for learning turbulence models from indirect observation data
Xin-Lei Zhang, Heng Xiao, Xiaodong Luo +1
In this work, we propose using an ensemble Kalman method to learn a nonlinear eddy viscosity model, represented as a tensor basis neural network, from velocity data. Data-driven tu…