## 4 Conclusions
(2006) [PERSON]; [PERSON]; [PERSON]; [PERSON] A dynamic subgrid-scale eddy viscosity model with a global model coefficient. _Phys. Fluids_**2006**, _18_, 215109. [CrossRef]
* [PERSON] et al. (2010) [PERSON]; [PERSON]; [PERSON] Dynamic global model for large eddy simulation of transient flow. _Phys. Fluids_**2010**, _22_, 075106. [CrossRef]
* [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON] Reynolds averaged turbulence modelling using deep neural networks with embedded invariance. _J. Fluid Mech._**2016**, _807_, 155-166. [CrossRef]
* [PERSON] and [PERSON] (2017) [PERSON]; [PERSON] A neural network approach for the blind deconvolution of turbulent flows. _J. Fluid Mech._**2017**, _831_, 151-181. [CrossRef]
* [PERSON] and [PERSON] (1999) [PERSON]; [PERSON] An approximate deconvolution procedure for large-eddy simulation. _Phys. Fluids_**1999**, _11_, 1699-1701. [CrossRef]
* [PERSON] and [PERSON] (2021) [PERSON]; [PERSON] Toward neural-network-based large eddy simulation: Application to turbulent channel flow. _J. Fluid Mech._**2021**, _914_, A16. [CrossRef]
* [PERSON] and [PERSON] (2017) [PERSON]; [PERSON] [PERSON] Searching for turbulence models by artificial neural network. _Phys. Rev. Fluids_**2017**, \(2\), 054604. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Investigations of data-driven closure for subgrid-scale stress in large-eddy simulation. _Phys. Fluids_**2018**, _30_, 125101. [CrossRef]
* [PERSON] et al. (2003) [PERSON]; [PERSON] [PERSON]; [PERSON] Neural networks based subgrid scale modeling in large eddy simulations. _Comput. Fluids_**2003**, _32_, 97-108. [CrossRef]
* [PERSON] et al. (1991) [PERSON]; [PERSON]; [PERSON]; [PERSON] A dynamic subgrid-scale eddy viscosity model. _Phys. Fluids A Fluid Dyn._**1991**, \(3\), 1760-1765. [CrossRef]
* [PERSON] (1992) [PERSON] A proposed modification of the [PERSON] subgrid-scale closure method. _Phys. Fluids A Fluid Dyn._**1992**, \(4\), 633-635. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON] Machine learning for fluid mechanics. _Annu. Rev. Fluid Mech._**2020**, _52_, 477-508. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON] [PERSON]; [PERSON] Super-resolution reconstruction of turbulent flows with machine learning. _J. Fluid Mech._**2019**, _870_, 106-120. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON] [PERSON]; [PERSON] Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows. _J. Fluid Mech._**2021**, _909_, A9. [CrossRef]
* [PERSON] and [PERSON] (2019) [PERSON]; [PERSON] Data-driven prediction of unsteady flow over a circular cylinder using deep learning. _J. Fluid Mech._**2019**, _879_, 217-254. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON] Prediction model of velocity field around circular cylinder over various Reynolds numbers by fusion convolutional neural networks based on pressure on the cylinder. _Phys. Fluids_**2018**, _30_, 047105. [CrossRef]
* [PERSON] et al. (2002) [PERSON]; [PERSON]; [PERSON] An implicit velocity decoupling procedure for the incompressible Navier-Stokes equations. _Int. J. Numer. Methods Fluids_**2002**, _38_, 125-138. [CrossRef]
* [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON] Flow around a helically twisted elliptic cylinder. _Phys. Fluids_**2016**, _28_, 053602. [CrossRef]
* [PERSON] and [PERSON] (2022) [PERSON]; [PERSON] Active proportional feedback control of turbulent flow over a circular cylinder with averaged velocity sensor. _Phys. Fluids_**2022**, _34_, 095133. [CrossRef]
* [PERSON] et al. (1994) [PERSON]; [PERSON]; [PERSON] Feedback control of von Karman vortex shedding behind a circular cylinder at low Reynolds numbers. _Phys. Fluids_**1994**, \(6\), 2390-2405. [CrossRef]
* [PERSON] and [PERSON] (2011) [PERSON]; [PERSON]; [PERSON] A proportional-integral-differential control of flow over a circular cylinder. _Philos. Trans. R. Soc. A_**2011**, _369_, 1540-1555.