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

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

Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

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

A dynamic regularized gradient model of the subgrid-scale stress tensor for large-eddy simulation

(2016) A. Vollant et al.   PHYSICS OF FLUIDS

Using field inversion to quantify functional errors in turbulence closures

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

Hybrid spectral-particle method for the turbulent transport of a passive scalar

(2013) J.-B. Lagaert et al.   JOURNAL OF COMPUTATIONAL PHYSICS

A dynamic regularized gradient model of the subgrid-scale scalar flux for large eddy simulations

(2013) G. Balarac et al.   PHYSICS OF FLUIDS

Development of a new dynamic procedure for the Clark model of the subgrid-scale scalar flux using the concept of optimal estimator

(2011) Y. Fabre et al.   PHYSICS OF FLUIDS

Invariants of the velocity-gradient, rate-of-strain, and rate-of-rotation tensors across the turbulent/nonturbulent interface in jets

(2008) Carlos B. da Silva et al.   PHYSICS OF FLUIDS