9 citations · 13 across the 14 of their papers we have counts for
4 papers · 2 filters
Convolutional Neural Networks For Turbulent Model Uncertainty Quantification
Minghan Chu, Weicheng Qian
Complex turbulent flow simulations are an integral aspect of the engineering design process. The mainstay of these simulations is represented by eddy viscosity based turbulence mod…
Physics Based & Machine Learning Methods For Uncertainty Estimation In Turbulence Modeling
Minghan Chu
Turbulent flows play an important role in many scientific and technological design problems. Both Sub-Grid Scale (SGS) models in Large Eddy Simulations (LES) and Reynolds Averaged…
Physics Constrained Deep Learning For Turbulence Model Uncertainty Quantification
Minghan Chu, Weicheng Qian
Engineering design and scientific analysis rely upon computer simulations of turbulent fluid flows using turbulence models. These turbulence models are empirical and approximate, l…
A Deep Learning Approach For Epistemic Uncertainty Quantification Of Turbulent Flow Simulations
Minghan Chu, Weicheng Qian
Simulations of complex turbulent flow are part and parcel of the engineering design process. Eddy viscosity based turbulence models represent the workhorse for these simulations. T…