Zonal Eddy Viscosity Models Based on Machine Learning

Large-eddy simulation of turbulent flow over a parametric set of bumps

(2019) Racheet Matai et al.   JOURNAL OF FLUID MECHANICS

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

(2017) Jin-Long Wu et al.   FLOW TURBULENCE AND COMBUSTION

The development of algebraic stress models using a novel evolutionary algorithm

(2017) J. Weatheritt et al.   INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW

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

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

Machine learning strategies for systems with invariance properties

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

A paradigm for data-driven predictive modeling using field inversion and machine learning

(2016) Eric J. Parish et al.   JOURNAL OF COMPUTATIONAL PHYSICS

A novel evolutionary algorithm applied to algebraic modifications of the RANS stress–strain relationship

(2016) Jack Weatheritt 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

Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty

(2015) J. Ling et al.   PHYSICS OF FLUIDS

Formulation of the k-w Turbulence Model Revisited

(2008) D. C. Wilcox   AIAA JOURNAL