I. Goodfellow, Y. Bengio, A. Courville, and Y. Bengio, Deep learning, vol.1, 2016.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, pp.1097-1105, 2012.

D. Ferrucci, E. Brown, J. Chu-carroll, J. Fan, D. Gondek et al., Building watson: an overview of the deepqa project, AI Mag, vol.31, issue.3, pp.59-79, 2010.

D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang et al., Mastering the game of go without human knowledge, Nature, vol.550, issue.7676, p.354, 2017.

W. S. Mcculloch and W. Pitts, A logical calculus of the ideas immanent in nervous activity, Bull. Math. Biophys, vol.5, issue.4, pp.115-133, 1943.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen et al., TensorFlow: large-scale machine learning on heterogeneous systems, software available from tensorflow.org, 2015.

H. W. Lin, M. Tegmark, and D. Rolnick, Why does deep and cheap learning work so well?, J. Stat. Phys, vol.168, issue.6, pp.1223-1247, 2017.

K. Duraisamy, Z. J. Zhang, and A. P. Singh, New approaches in turbulence and transition modeling using data-driven techniques, 53rd AIAA Aerospace Sciences Meeting, p.1284, 2015.

J. Ling, A. Kurzawski, and J. Templeton, Reynolds averaged turbulence modelling using deep neural networks with embedded invariance, J. Fluid Mech, vol.807, pp.155-166, 2016.

A. Vollant, G. Balarac, and C. Corre, Subgrid-scale scalar flux modelling based on optimal estimation theory and machine-learning procedures, J. Turbul, vol.18, issue.9, pp.854-878, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01539517

R. Maulik and O. San, A neural network approach for the blind deconvolution of turbulent flows, J. Fluid Mech, vol.831, pp.151-181, 2017.

M. Schoepplein, J. Weatheritt, R. Sandberg, M. Talei, and M. Klein, Application of an evolutionary algorithm to LES modelling of turbulent transport in premixed flames, J. Comput. Phys, vol.374, pp.1166-1179, 2018.

K. Duraisamy, G. Iaccarino, and H. Xiao, Turbulence modeling in the age of data, Ann. Rev. Fluid Mech, vol.51, pp.357-377, 2019.

A. D. Beck, D. G. Flad, and C. Munz, Deep neural networks for data-based turbulence models

T. Poinsot and D. Veynante, Theoretical and numerical combustion, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00270731

F. E. Marble and J. E. Broadwell, The coherent flame model for turbulent chemical reactions, 1977.

S. Candel, D. Veynante, F. Lacas, E. Maistret, N. Darabiha et al., Coherent flame model: applications and recent extensions, Advances in Combustion Modeling, Series on advances in mathematics for applied sciences, pp.19-64, 1990.

S. Pope, The evolution of surfaces in turbulence, Int. J. Eng. Sci, vol.26, issue.5, pp.445-469, 1988.

K. N. Bray and J. B. Moss, A unified statistical model of the premixed turbulent flame, Acta Astron, vol.4, pp.291-319, 1977.

N. Peters, Laminar flamelet concepts in turbulent combustion, Symp. (Int.) Combust, vol.21, pp.1231-1250, 1986.

J. M. Duclos, D. Veynante, and T. Poinsot, A comparison of flamelet models for premixed turbulent combustion, Combust. Flame, vol.95, pp.101-117, 1993.

G. Bruneaux, T. Poinsot, and J. H. Ferziger, Premixed flame-wall interaction in a turbulent channel flow: budget for the flame surface density evolution equation and modelling, J. Fluid Mech, vol.349, pp.191-219, 1997.

M. Boger, D. Veynante, H. Boughanem, and A. Trouvé, Direct numerical simulation analysis of flame surface density concept for large eddy simulation of turbulent premixed combustion, Symp. (Int.) Combust, vol.27, pp.917-927, 1998.

R. Knikker, D. Veynante, and C. Meneveau, A dynamic flame surface density model for large eddy simulation of turbulent premixed combustion, Phys. Fluids, vol.16, pp.91-94, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00270711

A. R. Kerstein, W. Ashurst, and F. A. Williams, Field equation for interface propagation in an unsteady homogeneous flow field, Phys. Rev. A, vol.37, issue.7, pp.2728-2731, 1988.

F. Gouldin, K. Bray, and J. Y. Chen, Chemical closure model for fractal flamelets, Combust. Flame, vol.77, p.241, 1989.

T. Poinsot, D. Veynante, and S. Candel, Quenching processes and premixed turbulent combustion diagrams, J. Fluid Mech, vol.228, pp.561-605, 1991.

O. L. Gulder and G. J. Smallwood, Inner cutoff scale of flame surface wrinkling in turbulent premixed flames, Combust. Flame, vol.103, pp.107-114, 1995.

F. Charlette, D. Veynante, and C. Meneveau, A power-law wrinkling model for LES of premixed turbulent combustion. Part I -non-dynamic formulation and initial tests, Combust. Flame, vol.131, pp.159-180, 2002.

G. Wang, M. Boileau, and D. Veynante, Implementation of a dynamic thickened flame model for large eddy simulations of turbulent premixed combustion, Combust. Flame, vol.158, issue.11, pp.2199-2213, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00659566

F. Charlette, C. Meneveau, and D. Veynante, A power-law flame wrinkling model for les of premixed turbulent combustion. Part II: dynamic formulation, Combust. Flame, vol.131, issue.1-2, pp.181-197, 2002.

W. K. Pratt, Digital image processing: PIKS scientific inside, vol.4, 2007.

O. Colin, F. Ducros, D. Veynante, and T. Poinsot, A thickened flame model for large eddy simulations of turbulent premixed combustion, Phys. Fluids, vol.12, issue.7, pp.1843-1863

T. Schønfeld and M. Rudgyard, Steady and unsteady flows simulations using the hybrid flow solver AVBP, AIAA J, vol.37, issue.11, pp.1378-1385, 1999.

L. Selle, G. Lartigue, T. Poinsot, R. Koch, K. U. Schildmacher et al., Compressible large-eddy simulation of turbulent combustion in complex geometry on unstructured meshes, Combust. Flame, vol.137, issue.4, pp.489-505, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00271666

O. Colin and M. Rudgyard, Development of high-order Taylor-Galerkin schemes for unsteady calculations, J. Comput. Phys, vol.162, issue.2, pp.338-371

T. Poinsot and S. Lele, Boundary conditions for direct simulations of compressible viscous flows, J. Comput. Phys, vol.101, issue.1, pp.104-129, 1992.

V. Granet, O. Vermorel, T. Leonard, L. Gicquel, and T. Poinsot, Comparison of nonreflecting outlet boundary conditions for compressible solvers on unstructured grids, AIAA J, vol.48, issue.10, pp.2348-2364, 2010.

B. Franzelli, E. Riber, L. Y. Gicquel, and T. Poinsot, Large eddy simulation of combustion instabilities in a lean partially premixed swirled flame, Combust. Flame, vol.159, issue.2, pp.621-637, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00811961

S. B. Pope, Turbulent flows, 2000.
URL : https://hal.archives-ouvertes.fr/hal-00338511

T. Passot and A. Pouquet, Numerical simulation of compressible homogeneous flows in the turbulent regime, J. Fluid Mech, vol.181, pp.441-466, 1987.

M. Everingham, L. V. Gool, C. K. Williams, J. Winn, and A. Zisserman, The Pascal Visual Object Classes (VOC) challenge, Int. J. Comput. Vis, vol.88, issue.2, pp.303-338, 2010.

J. Long, E. Shelhamer, and T. Darrell, Fully convolutional networks for semantic segmentation, IEEE Conference on Computer Vision and Pattern Recognition, pp.3431-3440, 2015.

O. Ronneberger, P. Fischer, and T. Brox, U-net: convolutional networks for biomedical image segmentation, International Conference on Medical image Computing and Computer-assisted Intervention, pp.234-241, 2015.

L. C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs, IEEE Trans. Pattern Anal. Mach. Intell, vol.40, issue.4, pp.834-848, 2018.

Z. Liu, X. Li, P. Luo, C. Loy, and X. Tang, Semantic image segmentation via deep parsing network, Proceedings of the IEEE International Conference on Computer Vision, pp.1377-1385, 2015.

D. P. Kingma and J. Ba, A method for stochastic optimization, Proceedings of the 3rd International Conference on Learning Representations, 2015.

T. Poinsot, A. Trouvé, D. Veynante, S. Candel, and E. Esposito, Vortex driven acoustically coupled combustion instabilities, J. Fluid Mech, vol.177, pp.265-292, 1987.