J. D. Müller, J. Hückelheim, and O. Mykhaskiv, STAMPS: a finite-volume solver framework for adjoint codes derived with source-transformation AD, 2018 Multidisciplinary Analysis and Optimization Conference, p.2928, 2018.

G. Kenway, C. Mader, P. He, and J. Martins, Effective adjoint approaches for computational fluid dynamics, Prog. Aerosp. Sci, vol.110, p.100542, 2019.

S. Xu, D. Radford, M. Meyer, and J. D. Müller, Stabilisation of discrete steady adjoint solvers, J. Comput. Phys, vol.299, pp.75-195, 2015.

I. Charpentier, Checkpointing schemes for adjoint codes: application to the meteorological model Meso-NH, SIAM J. Sci. Comput, vol.22, issue.6, pp.2135-2151, 2001.

P. Heimbach, C. Hill, and R. Giering, An efficient exact adjoint of the parallel MIT general circulation model, generated via automatic differentiation, Future Gener, 2005.

, Comput. Syst, vol.21, issue.8, pp.1356-1371

J. Hückelheim, L. Hascoët, and J. D. Müller, Algorithmic differentiation of code with multiple context-specific activities, ACM Trans. Math. Softw, vol.43, issue.4, 2017.

J. Utke, L. Hascoët, P. Heimbach, C. Hill, P. Hovland et al., Toward Adjoinable MPI, 2009 IEEE International Symposium on Parallel Distributed Processing, pp.1-8, 2009.

F. Witherden and A. Jameson, Future directions in computational fluid dynamics, 23rd AIAA Computational Fluid Dynamics Conference, p.3791, 2017.

A. Cassagne, J. F. Boussuge, N. Villedieu, G. Puigt, I. Genot et al., Jaguar: a new CFD code dedicated to massively parallel high-order LES computations on complex geometry, The 50th 3AF International Conference on Applied Aerodynamics, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01965640

V. Brunet, E. Croner, A. Minot, J. De-laborderie, E. Lippinois et al., Comparison of various CFD codes for LES simulations of turbomachinery: from inviscid vortex convection to multi-stage compressor, Turbomachinery Technical Conference and Exposition, vol.2, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02003192

L. Hascoët and V. Pascual, The Tapenade automatic differentiation tool: principles, model, and specification, ACM Trans. Math. Softw, vol.39, issue.3, 2013.

A. Griewank and A. Walther, Evaluating derivatives: principles and techniques of algorithmic differentiation, 2008.

L. Hascoët and J. Utke, Programming language features, usage patterns, and the efficiency of generated adjoint code, Optim. Methods Softw, vol.31, issue.5, pp.885-903, 2016.

A. Walther and A. Griewank, Advantages of binomial checkpointing for memory-reduced adjoint calculations, Numerical mathematics and advanced applications, pp.834-843, 2004.

G. Aupy, J. Herrmann, P. Hovland, and Y. Robert, Optimal multistage algorithm for adjoint computation, SIAM J. Sci. Comput, vol.38, issue.3, pp.232-255, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01147155

P. Hovland and C. Bischof, Automatic differentiation for message-passing parallel programs, Proceedings of the First Merged International Parallel Processing Symposium and Symposium on Parallel and Distributed Processing, pp.98-104, 1998.

P. Heimbach, C. Hill, and R. Giering, Automatic generation of efficient adjoint code for a parallel Navier-Stokes solver, Computational Science ICCS, pp.1019-1028, 2002.

M. Schanen, U. Naumann, L. Hascoët, and J. Utke, Interpretative adjoints for numerical simulation codes using MPI, Procedia Comput. Sci, vol.1, issue.1, pp.1825-1833, 2010.

M. Schanen and U. Naumann, A wish list for efficient adjoints of one-sided MPI communication, European MPI Users' Group Meeting, pp.248-257, 2012.

M. Schanen, M. Förster, and U. Naumann, Second-order algorithmic differentiation by source transformation of MPI code, European MPI Users' Group Meeting, pp.257-264, 2010.

M. Towara, M. Schanen, and U. Naumann, MPI-parallel discrete adjoint OpenFOAM, Procedia Comput. Sci, vol.51, pp.19-28, 2015.

M. Sagebaum, T. Albring, and N. Gauger, High-performance derivative computations using CoDiPack, 2017.

T. Albring, M. Sagebaum, and N. Gauger, Efficient aerodynamic design using the discrete adjoint method in SU2, 17th AIAA/ISSMO multidisciplinary analysis and optimization conference, p.3518, 2016.

J. Hückelheim, P. Hovland, M. Strout, and J. D. Müller, Parallelizable adjoint stencil computations using transposed forward-mode algorithmic differentiation, Optim. Methods Softw, vol.33, issue.4-6, pp.672-693, 2018.

J. Berland, C. Bogey, and C. Bailly, Lowdissipation and low-dispersion fourth-order Runge-Kutta algorithm, Comput. & Fluids, vol.35, issue.10, pp.1459-1463, 2006.
URL : https://hal.archives-ouvertes.fr/hal-02342209