102 citations · 262 across the 10 of their papers we have counts for
10 papers
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…
Ensemble gradient for learning turbulence models from indirect observations
Carlos A. Michelén Ströfer, Xin-Lei Zhang, Heng Xiao
Training data-driven turbulence models with high fidelity Reynolds stress can be impractical and recently such models have been trained with velocity and pressure measurements. For…
Assimilation of disparate data for enhanced reconstruction of turbulent mean flows
Xin-Lei Zhang, Heng Xiao, Guo-Wei He +1
Reconstruction of turbulent flow based on data assimilation methods is of significant importance for improving the estimation of flow characteristics by incorporating limited obser…
DAFI: An Open-Source Framework for Ensemble-Based Data Assimilation and Field Inversion
Carlos A. Michelén Ströfer, Xin-Lei Zhang, Heng Xiao
In many areas of science and engineering, it is a common task to infer physical fields from sparse observations. This paper presents the DAFI code intended as a flexible framework…
Evaluation of ensemble methods for quantifying uncertainties in steady-state CFD applications with small ensemble sizes
Xinlei Zhang, Heng Xiao, Thomas Gomez +1
Bayesian uncertainty quantification (UQ) is of interest to industry and academia as it provides a framework for quantifying and reducing the uncertainty in computational models by…
Enforcing Boundary Conditions on Physical Fields in Bayesian Inversion
Carlos A. Michelén Ströfer, Xinlei Zhang, Heng Xiao +1
Inverse problems in computational mechanics consist of inferring physical fields that are latent in the model describing some observable fields. For instance, an inverse problem of…
Regularized Ensemble Kalman Methods for Inverse Problems
Xin-Lei Zhang, Carlos Michelén-Ströfer, Heng Xiao
Inverse problems are common and important in many applications in computational physics but are inherently ill-posed with many possible model parameters resulting in satisfactory r…