Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework

Data-Driven Integral Boundary-Layer Modeling for Airfoil Performance Prediction in Laminar Regime

(2018) Alexandre N. Marques et al.   AIAA JOURNAL

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

Subgrid-scale scalar flux modelling based on optimal estimation theory and machine-learning procedures

(2017) A. Vollant et al.   JOURNAL OF TURBULENCE

Searching for turbulence models by artificial neural network

(2017) Masataka Gamahara et al.   Physical Review Fluids

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

A Bayesian Calibration–Prediction Method for Reducing Model-Form Uncertainties with Application in RANS Simulations

(2016) Jin-Long Wu et al.   FLOW TURBULENCE AND COMBUSTION

Using statistical learning to close two-fluid multiphase flow equations for bubbly flows in vertical channels

(2016) Ming Ma et al.   INTERNATIONAL JOURNAL OF MULTIPHASE FLOW

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

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

Using field inversion to quantify functional errors in turbulence closures

(2016) Anand Pratap Singh et al.   PHYSICS OF FLUIDS

On the accuracy of RANS simulations with DNS data

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

Direct numerical simulation of turbulent channel flow up to

(2015) Myoungkyu Lee et al.   JOURNAL OF FLUID MECHANICS

Using statistical learning to close two-fluid multiphase flow equations for a simple bubbly system

(2015) Ming Ma et al.   PHYSICS OF FLUIDS

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

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

Philosophies and fallacies in turbulence modeling

(2015) Philippe R. Spalart   PROGRESS IN AEROSPACE SCIENCES

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

(2013) Michael Emory et al.   PHYSICS OF FLUIDS

Reynolds number dependence of mean flow structure in square duct turbulence

(2010) ALFREDO PINELLI et al.   JOURNAL OF FLUID MECHANICS

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

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