Application of deep learning method to Reynolds stress models of channel flow based on reduced-order modeling of DNS data

Five-equation and robust three-equation methods for solution verification of large eddy simulation

(2018) Rabijit Dutta et al.   Journal of Hydrodynamics

URANS simulations of the tip-leakage cavitating flow with verification and validation procedures

(2018) Huai-yu Cheng et al.   Journal of Hydrodynamics

Numerical simulation of transient turbulent cavitating flows with special emphasis on shock wave dynamics considering the water/vapor compressibility

(2018) Chang-chang Wang et al.   Journal of Hydrodynamics

Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier–Stokes simulations: A data-driven, physics-informed Bayesian approach

(2016) H. Xiao et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

(2016) Julia Ling et al.   JOURNAL OF FLUID MECHANICS

Uncertainty Analysis and Data-Driven Model Advances for a Jet-in-Crossflow

(2016) Julia Ling et al.   JOURNAL OF TURBOMACHINERY-TRANSACTIONS OF THE ASME

Deep learning

(2015) Yann LeCun et al.   NATURE

Philosophies and fallacies in turbulence modeling

(2015) Philippe R. Spalart   PROGRESS IN AEROSPACE SCIENCES