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C. A. Mader, Martins, J. R. R. A., and Marta, A. C., Towards Aircraft Design Using an Automatic Discrete Adjoint Solver, Proceedings of the 18th AIAA Computational Fluid Dynamics Conference. Miami, FL, 2007.
C. A. Mader, Martins, J. R. R. A., and Marta, A. C., Towards Aerodynamic Shape Optimization of an Oblique Wing Using the ADjoint Approach, Proceedings of the CASI Conference. {T}oronto, {ON}, 2007.
Tools for Automatic Differentiation. .
L. Hascoët, TAPENADE: A tool for Automatic Differentiation of programs, Proceedings of 4$^{th}$ European Congress on Computational Methods, ECCOMAS'2004, Jyvaskyla, Finland. 2004.
L. Hascoët and Pascual, V., TAPENADE 2.1 User's Guide. INRIA, 2004.
C. A. Mader and Martins, J. R. R. A., Stability-Constrained Aerodynamic Shape Optimization of Flying Wings, Proceedings of the CASI Conference. {M}ontreal, {QC}, 2011.
M. Fagan and Carle, A., Reducing Reverse-Mode Memory Requirements by Using Profile-Driven Checkpointing, Future Generation Comp. Syst., vol. 21. pp. 1380–1390, 2005.
P. Cusdin and Müller, J. - D., On the Performance of Discrete Adjoint CFD Codes using Automatic Differentiation, International Journal of Numerical Methods in Fluids, vol. 47. pp. 939-945, 2005.
K. Kubota, PADRE2 –- FORTRAN Precompiler for Automatic Differentiation and Estimates of Rounding Errors, Computational Differentiation: Techniques, Applications, and Tools. {SIAM}, Philadelphia, Pennsylvania, pp. 367–374, 1996.
S. Brown, OPRAD –- A Users Guide to the OPtima Reverse Automatic Differentiation Tool. Numerical Optimization Centre, University of Hertfordsshire, 1995.
M. Bartholomew-Biggs, OPFAD –- A Users Guide to the OPtima Forward Automatic Differentiation Tool. Numerical Optimization Centre, University of Hertfordsshire, 1995.
A. C. Marta, Mader, C. A., Martins, J. R. R. A., van der Weide, E., and Alonso, J. J., A methodology for the development of discrete adjoint solvers using automatic differentiation tools, International Journal of Computational Fluid Dynamics, vol. 21. pp. 307-327, 2007.
P. Cusdin and Müller, J. - D., An Introduction to Tangent and Adjoint Mode Automatic Differentiation for Computational Fluid Dynamics. QUB School of Aeronautical Engineering, 2003.
A. Carle and Fagan, M., Improving Derivative Performance for CFD by Using Simplified Recurrences, Computational Differentiation: Techniques, Applications, and Tools. SIAM, Philadelphia, PA, pp. 343–351, 1996.
T. Beck and Fischer, H., The if-problem in automatic differentiation, Journal of Computational and Applied Mathematics, vol. 50. pp. 119–131, 1994.
J. D. Pryce and Tadjouddine, E. M., Fast AD Jacobians by compact LU factorization, {SIAM} Journal on Scientific Computing. 2007.
E. Tijskens, Ramon, H., and De Baerdemaeker, J., Efficient Operator Overloading AD for Solving Nonlinear PDEs, Automatic Differentiation of Algorithms: From Simulation to Optimization. Springer, New York, NY, pp. 167–172, 2001.
C. A. Mader and Martins, J. R. R. A., A Discrete Adjoint Formulation for Stability Derivatives Using the ADjoint Approach, Proceedings of the CASI Conference. {K}anata, {ON}, 2009.
C. H. Bischof, Corliss, G. F., Green, L., Griewank, A., Haigler, K., and Newman, P., Automatic Differentiation of Advanced CFD Codes for Multidisciplinary Design, Journal on Computing Systems in Engineering, vol. 3. pp. 625–638, 1992.
C. Bischof, Corliss, G., Green, L., Griewank, A., Haigler, K., and Newman, P., Automatic Differentiation of Advanced CFD Codes for Multidisciplinary Design. Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, Ill., 1993.
B. Mohammadi, Malé, J. - M., and Rostaing-Schmidt, N., Automatic Differentiation in Direct and Reverse Modes: Application to Optimum Shapes Design in Fluid Mechanics, Computational Differentiation: Techniques, Applications, and Tools. SIAM, Philadelphia, PA, pp. 309–318, 1996.
G. Corliss, Faure, C., Griewank, A., Hascoët, L., and Naumann, U., Automatic Differentiation: From Simulation to Optimization. Springer, 2001.
A. Griewank, Juedes, D., and Utke, J., Algorithm 755: ADOL-C: a package for the automatic differentiation of algorithms written in C\slash C++, ACM Transactions on Mathematical Software, vol. 22. pp. 131–167, 1996.
C. Bischof, Carle, A., Corliss, G., Grienwank, A., and Hoveland, P., ADIFOR: Generating Derivative Codes from Fortran Programs, Scientific Programming, vol. 1. pp. 11–29, 1992.
C. Bischof, Corliss, G., and Grienwank, A., ADIFOR Exception Handling. Argonne Technical Memorandum, 1991.
A. Carle and Fagan, M., ADIFOR 3.0 Overview. Rice University, 2000.
C. Bischof, Khademi, P., Mauer, A., and Carle, A., Adifor 2.0: automatic differentiation of Fortran 77 programs, Computational Science Engineering, IEEE, vol. 3. pp. 18–32, 1996.
C. H. Bischof, Roh, L., and Mauer-Oats, A. J., ADIC: an extensible automatic differentiation tool for ANSI-C, Software –- Practice and Experience, vol. 27. pp. 1427–1456, 1997.
C. A. Mader, ADjoint: An Approach for the Rapid Development of Discrete Adjoint Solvers. University of Toronto Institute for Aerospace Studies, {T}oronto, {ON}, 2007.
J. D. Pryce and Reid, J. K., AD01, a Fortran 90 Code for Automatic Differentiation. Rutherford Appleton Laboratory, Chilton, Didcot, Oxfordshire, OX11 OQX, U.K., 1998.