Crossref Citations
This article has been cited by the following publications. This list is generated based on data provided by
Crossref.
Bode, Mathis
Gauding, Michael
Kleinheinz, Konstantin
and
Pitsch, Heinz
2019.
High Performance Computing.
Vol. 11887,
Issue. ,
p.
541.
Srinivasan, P. A.
Guastoni, L.
Azizpour, H.
Schlatter, P.
and
Vinuesa, R.
2019.
Predictions of turbulent shear flows using deep neural networks.
Physical Review Fluids,
Vol. 4,
Issue. 5,
Brenner, M. P.
Eldredge, J. D.
and
Freund, J. B.
2019.
Perspective on machine learning for advancing fluid mechanics.
Physical Review Fluids,
Vol. 4,
Issue. 10,
Jiménez, Javier
2020.
Monte Carlo science.
Journal of Turbulence,
Vol. 21,
Issue. 9-10,
p.
544.
Jiménez, Javier
2020.
Dipoles and streams in two-dimensional turbulence.
Journal of Fluid Mechanics,
Vol. 904,
Issue. ,
Matharu, Pritpal
and
Protas, Bartosz
2020.
Optimal Closures in a Simple Model for Turbulent Flows.
SIAM Journal on Scientific Computing,
Vol. 42,
Issue. 1,
p.
B250.
Guastoni, Luca
Encinar, Miguel P.
Schlatter, Philipp
Azizpour, Hossein
and
Vinuesa, Ricardo
2020.
Prediction of wall-bounded turbulence from wall quantities using convolutional neural networks.
Journal of Physics: Conference Series,
Vol. 1522,
Issue. 1,
p.
012022.
Pastor, R
Vela-Martín, A
and
Flores, O
2020.
Wall-bounded turbulence control: statistical characterisation of actions/states.
Journal of Physics: Conference Series,
Vol. 1522,
Issue. 1,
p.
012014.
Fang, Rui
Sondak, David
Protopapas, Pavlos
and
Succi, Sauro
2020.
Neural network models for the anisotropic Reynolds stress tensor in turbulent channel flow.
Journal of Turbulence,
Vol. 21,
Issue. 9-10,
p.
525.
Jiménez, Javier
2020.
Computers and turbulence.
European Journal of Mechanics - B/Fluids,
Vol. 79,
Issue. ,
p.
1.
Gopalakrishnan Meena, Muralikrishnan
and
Taira, Kunihiko
2021.
Identifying vortical network connectors for turbulent flow modification.
Journal of Fluid Mechanics,
Vol. 915,
Issue. ,
Yeh, Chi-An
Gopalakrishnan Meena, Muralikrishnan
and
Taira, Kunihiko
2021.
Network broadcast analysis and control of turbulent flows.
Journal of Fluid Mechanics,
Vol. 910,
Issue. ,
Jiang, Chao
Vinuesa, Ricardo
Chen, Ruilin
Mi, Junyi
Laima, Shujin
and
Li, Hui
2021.
An interpretable framework of data-driven turbulence modeling using deep neural networks.
Physics of Fluids,
Vol. 33,
Issue. 5,
Güemes, A.
Discetti, S.
Ianiro, A.
Sirmacek, B.
Azizpour, H.
and
Vinuesa, R.
2021.
From coarse wall measurements to turbulent velocity fields through deep learning.
Physics of Fluids,
Vol. 33,
Issue. 7,
Zhou, Ye
2021.
Turbulence theories and statistical closure approaches.
Physics Reports,
Vol. 935,
Issue. ,
p.
1.
Jiménez, Javier
2021.
Collective organization and screening in two-dimensional turbulence.
Physical Review Fluids,
Vol. 6,
Issue. 8,
Guastoni, Luca
Güemes, Alejandro
Ianiro, Andrea
Discetti, Stefano
Schlatter, Philipp
Azizpour, Hossein
and
Vinuesa, Ricardo
2021.
Convolutional-network models to predict wall-bounded turbulence from wall quantities.
Journal of Fluid Mechanics,
Vol. 928,
Issue. ,
Matharu, Pritpal
and
Protas, Bartosz
2022.
Optimal eddy viscosity in closure models for two-dimensional turbulent flows.
Physical Review Fluids,
Vol. 7,
Issue. 4,
Saeed, Ahmad
Farooq, Hamayun
Akhtar, Imran
and
Bangash, Zafar
2022.
Deep learning-based reduced-order model for turbulent flows.
p.
821.
Eivazi, Hamidreza
Le Clainche, Soledad
Hoyas, Sergio
and
Vinuesa, Ricardo
2022.
Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows.
Expert Systems with Applications,
Vol. 202,
Issue. ,
p.
117038.