R. M. Natalia-m-alexandrov, C. R. Lewis, . Gumbert, P. Lawrence-l-green, and . Newman, Approximation and model management in aerodynamic optimization with variable-fidelity models, Journal of Aircraft, vol.38, issue.6, pp.1093-1101, 2001.

R. Steven and F. Allmaras, Modifications and clarifications for the implementation of the spalart-allmaras turbulence model, Seventh international conference on computational fluid dynamics (ICCFD7), pp.1-11, 2012.

E. Ampellio, F. Bertini, A. Ferrero, F. Larocca, and L. Vassio, Turbomachinery design by a swarm-based optimization method coupled with a cfd solver, Advances in aircraft and spacecraft science, vol.3, issue.2, pp.149-170, 2016.

D. Amsallem and C. Farhat, Interpolation method for adapting reduced-order models and application to aeroelasticity, AIAA journal, vol.46, issue.7, pp.1803-1813, 2008.

D. Amsallem, J. Matthew, C. Zahr, and . Farhat, Nonlinear model order reduction based on local reduced-order bases, International Journal for Numerical Methods in Engineering, vol.92, issue.10, pp.891-916, 2012.

D. Amsallem, M. Zahr, Y. Choi, and C. Farhat, Design optimization using hyper-reduced-order models. Structural and Multidisciplinary Optimization, vol.51, pp.919-940, 2015.

A. Badías, D. González, I. Alfaro, F. Chinesta, and E. Cueto, Local proper generalized decomposition, AIP Conference Proceedings, vol.1896, p.170007, 2017.

J. Baiges, R. Codina, and S. Idelsohn, A domain decomposition strategy for reduced order models. application to the incompressible navier-stokes equations, Computer Methods in Applied Mechanics and Engineering, vol.267, pp.23-42, 2013.

M. Barrault, Y. Maday, N. C. Nguyen, and A. Patera, An 'empirical interpolation' method: application to efficient reduced-basis discretization of partial differential equations, Comptes Rendus Mathematique, vol.339, issue.9, pp.667-672, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00021702

F. Bassi, L. Botti, A. Colombo, D. Pietro, and P. Tesini, On the flexibility of agglomeration based physical space discontinuous galerkin discretizations, Journal of Computational Physics, vol.231, issue.1, pp.45-65, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00562219

M. Bergmann, C. Bruneau, and A. Iollo, Enablers for robust pod models, Journal of Computational Physics, vol.228, issue.2, pp.516-538, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00338203

M. Bergmann, T. Colin, A. Iollo, D. Lombardi, O. Saut et al., Reduced order models at work in aeronautics and medicine, Reduced Order Methods for Modeling and Computational Reduction, pp.305-332, 2014.

M. Bergmann, A. Ferrero, A. Iollo, E. Lombardi, A. Scardigli et al., A zonal galerkin-free pod model for incompressible flows, Journal of Computational Physics, vol.352, pp.301-325, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01668546

T. Braconnier, J. Ferrier, . Jouhaud, P. Montagnac, and . Sagaut, Towards an adaptive pod/svd surrogate model for aeronautic design, Computers & Fluids, vol.40, issue.1, pp.195-209, 2011.
URL : https://hal.archives-ouvertes.fr/hal-01298892

M. Buffoni and K. Willcox, Projection-based model reduction for reacting flows, 40th Fluid Dynamics Conference and Exhibit, p.5008, 2010.

M. Buffoni, H. Telib, and A. Iollo, Iterative methods for model reduction by domain decomposition, Computers & Fluids, vol.38, issue.6, pp.1160-1167, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00193962

A. Burbeau and . Sagaut, A dynamic p-adaptive discontinuous galerkin method for viscous flow with shocks, Computers & fluids, vol.34, issue.4-5, pp.401-417, 2005.

A. Caiazzo, T. Iliescu, J. Volker, and S. Schyschlowa, A numerical investigation of velocity-pressure reduced order models for incompressible flows, Journal of Computational Physics, vol.259, pp.598-616, 2014.

K. Carlberg and C. Farhat, A compact proper orthogonal decomposition basis for optimization-oriented reduced-order models, 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, p.5964, 2008.

K. Carlberg, C. Bou-mosleh, and C. Farhat, Efficient non-linear model reduction via a least-squares petrov-galerkin projection and compressive tensor approximations, International Journal for Numerical Methods in Engineering, vol.86, issue.2, pp.155-181, 2011.

S. Chaturantabut and D. C. Sorensen, Nonlinear model reduction via discrete empirical interpolation, SIAM Journal on Scientific Computing, vol.32, issue.5, pp.2737-2764, 2010.

F. Chinesta, A. Leygue, F. Bordeu, J. V. Aguado, E. Cueto et al., Pgd-based computational vademecum for efficient design, optimization and control, Archives of Computational Methods in Engineering, vol.20, issue.1, pp.31-59, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01515083

R. Dupuis, J. Jouhaud, and P. Sagaut, Aerodynamic data predictions for transonic flows via a machine-learning-based surrogate model, 2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, p.1905, 2018.

A. Ferrero and F. Larocca, Feedback filtering in discontinuous galerkin methods for euler equations. Progress in Computational Fluid Dynamics, an International Journal, vol.16, issue.1, pp.14-25, 2016.

A. Ferrero and F. Larocca, Adaptive cfd schemes for aerospace propulsion, Journal of Physics: Conference Series, vol.841, issue.1, 2017.

A. Ferrero, F. Larocca, and G. Puppo, A robust and adaptive recovery-based discontinuous galerkin method for the numerical solution of convection-diffusion equations, International Journal for Numerical Methods in Fluids, vol.77, issue.2, pp.63-91, 2015.

A. Ferrero, F. Larocca, and V. Bernaschek, Unstructured discretisation of a nonlocal transition model for turbomachinery flows. Advances in aircraft and spacecraft science, vol.4, pp.555-571, 2017.

C. Geuzaine and J. Remacle, Gmsh: A 3-d finite element mesh generator with built-in pre-and post-processing facilities, International journal for numerical methods in engineering, vol.79, issue.11, pp.1309-1331, 2009.

R. Hartmann and P. Houston, Adaptive discontinuous galerkin finite element methods for the compressible euler equations, Journal of Computational Physics, vol.183, issue.2, pp.508-532, 2002.

K. Hillewaert, C. Carton-de-wiart, G. Verheylewegen, and T. Arts, Assessment of a high-order discontinuous galerkin method for the direct numerical simulation of transition at low-reynolds number in the t106c high-lift low pressure turbine cascade, ASME Turbo Expo 2014: Turbine Technical Conference and Exposition, pp.2-39, 2014.

M. Ilak and C. W. Rowley, Modeling of transitional channel flow using balanced proper orthogonal decomposition, Physics of Fluids, vol.20, issue.3, p.34103, 2008.

M. Donald-r-jones, W. Schonlau, and . Welch, Efficient global optimization of expensive black-box functions, Journal of Global optimization, vol.13, issue.4, pp.455-492, 1998.

S. Kaulmann, M. Ohlberger, and B. Haasdonk, A new local reduced basis discontinuous galerkin approach for heterogeneous multiscale problems, Comptes Rendus Mathematique, vol.349, pp.1233-1238, 2011.

D. Lukarski and N. Trost, , 2014.

Y. Maday and . Einar-m-ronquist, The reduced basis element method: application to a thermal fin problem, SIAM Journal on Scientific Computing, vol.26, issue.1, pp.240-258, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00021699

P. Bernd-r-noack, P. Papas, and . Monkewitz, The need for a pressure-term representation in empirical galerkin models of incompressible shear flows, Journal of Fluid Mechanics, vol.523, pp.339-365, 2005.

S. Osher and F. Solomon, Upwind difference schemes for hyperbolic systems of conservation laws, Mathematics of computation, vol.38, issue.158, pp.339-374, 1982.

M. Pandolfi, A contribution to the numerical prediction of unsteady flows, AIAA journal, vol.22, issue.5, pp.602-610, 1984.

B. Peherstorfer, K. Willcox, and M. Gunzburger, Survey of multifidelity methods in uncertainty propagation, inference and optimization, 2016.

A. Quarteroni and G. Rozza, Reduced order methods for modeling and computational reduction, vol.9, 2014.

A. Quarteroni, G. Rozza, and A. Manzoni, Certified reduced basis approximation for parametrized partial differential equations and applications, Journal of Mathematics in Industry, vol.1, issue.1, p.3, 2011.

. Clarence-w-rowley, Model reduction for fluids, using balanced proper orthogonal decomposition, International Journal of Bifurcation and Chaos, vol.15, issue.03, pp.997-1013, 2005.

T. Clarence-w-rowley, R. Colonius, and . Murray, Model reduction for compressible flows using pod and galerkin projection, Physica D: Nonlinear Phenomena, vol.189, issue.1, pp.115-129, 2004.

D. Ryckelynck, A priori hyperreduction method: an adaptive approach, Journal of computational physics, vol.202, issue.1, pp.346-366, 2005.
DOI : 10.1016/j.jcp.2004.07.015

J. Peter and . Schmid, Dynamic mode decomposition of numerical and experimental data, Journal of fluid mechanics, vol.656, pp.5-28, 2010.

S. Sirisup, G. E. Karniadakis, D. Xiu, and . Kevrekidis, Equationfree/galerkin-free pod-assisted computation of incompressible flows, Journal of Computational Physics, vol.207, issue.2, pp.568-587, 2005.

L. Sirovich, Turbulence and the dynamics of coherent structures part i: coherent structures, Quarterly of applied mathematics, vol.45, issue.3, pp.561-571, 1987.

W. A. Timmer and . Van-rooij, Summary of the delft university wind turbine dedicated airfoils, Journal of solar energy engineering, vol.125, issue.4, pp.488-496, 2003.
DOI : 10.2514/6.2003-352

M. Vasile, E. Minisci, D. Quagliarella, M. Guénot, I. Lepot et al., Adaptive sampling strategies for nonintrusive pod-based surrogates. Engineering computations, vol.30, pp.521-547, 2013.

L. Wang and D. J. Mavriplis, Adjoint-based h-p adaptive discontinuous galerkin methods for the 2d compressible euler equations, Journal of Computational Physics, vol.228, issue.20, pp.7643-7661, 2009.

Z. Wang, I. Akhtar, J. Borggaard, and T. Iliescu, Two-level discretizations of nonlinear closure models for proper orthogonal decomposition, Journal of Computational Physics, vol.230, issue.1, pp.126-146, 2011.

D. Wells, . Wang, T. Xie, and . Iliescu, An evolve-then-filter regularized reduced order model for convection-dominated flows, International Journal for Numerical Methods in Fluids, vol.84, issue.10, pp.598-615, 2017.
DOI : 10.1002/fld.4363

K. Willcox and J. Peraire, Balanced model reduction via the proper orthogonal decomposition, AIAA journal, vol.40, issue.11, pp.2323-2330, 2002.
DOI : 10.2514/6.2001-2611

J. Matthew, P. Zahr, C. Avery, and . Farhat, A multilevel projection-based model order reduction framework for nonlinear dynamic multiscale problems in structural and solid mechanics, International Journal for Numerical Methods in Engineering, vol.112, issue.8, pp.855-881, 2017.

. Zhao-zhan, G. Wagdi, M. Habashi, and . Fossati, Local reduced-order modeling and iterative sampling for parametric analyses of aero-icing problems, AIAA Journal, vol.53, issue.8, pp.2174-2185, 2015.