A Priori Assessment of Prediction Confidence for Data-Driven Turbulence Modeling

Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data

(2017) Jian-Xun Wang et al.   Physical Review Fluids

A methodology to evaluate statistical errors in DNS data of plane channel flows

(2016) Roney L. Thompson et al.   COMPUTERS & FLUIDS

Machine learning strategies for systems with invariance properties

(2016) Julia Ling et al.   JOURNAL OF COMPUTATIONAL PHYSICS

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

On the accuracy of RANS simulations with DNS data

(2016) Svetlana V. Poroseva et al.   PHYSICS OF FLUIDS

Modeling of structural uncertainties in Reynolds-averaged Navier-Stokes closures

(2013) Michael Emory et al.   PHYSICS OF FLUIDS

A framework for epistemic uncertainty quantification of turbulent scalar flux models for Reynolds-averaged Navier-Stokes simulations

(2013) C. Gorlé et al.   PHYSICS OF FLUIDS

Large-eddy simulation of turbulent boundary layer separation from a rounded step

(2012) Yacine Bentaleb et al.   JOURNAL OF TURBULENCE

Flow over periodic hills – Numerical and experimental study in a wide range of Reynolds numbers

(2008) M. Breuer et al.   COMPUTERS & FLUIDS