References in zbMATH (referenced in 137 articles , 1 standard article )

Showing results 121 to 137 of 137.
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  1. Pearlmutter, Barak A.; Siskind, Jeffrey Mark: Using programming language theory to make automatic differentiation sound and efficient (2008)
  2. Potapov, M. A.; Shatokhin, E. A.: Some methods of automatic analysis and controllable transformation of programs (2008)
  3. Désidéri, Jean-Antoine; El Majd, Badr Abou; Janka, Aleš: Nested and self-adaptive Bézier parameterizations for shape optimization (2007)
  4. Gejadze, I.; Le Dimet, F.-X.; Shutyaev, V.: On error covariances in variational data assimilation (2007)
  5. Gejadze, I. Yu.; Monnier, J.: On a 2D `zoom’ for the 1D shallow water model: coupling and data assimilation (2007)
  6. Marta, A. C.; Mader, C. A.; Martins, J. R. R. A.; Van Der Weide, E.; Alonso, J. J.: A methodology for the development of discrete adjoint solvers using automatic differentiation tools (2007)
  7. Mohammadi, B.: Global optimization, level set dynamics, incomplete sensitivity and regularity control (2007)
  8. Tber, Moulay Hicham; Hascoët, Laurent; Vidard, Arthur; Dauvergne, Benjamin: Building the tangent and adjoint codes of the ocean general circulation model OPA with the automatic differentiation tool TAPENADE (2007) ioport
  9. Bartlett, Roscoe A.; Gay, David M.; Phipps, Eric T.: Automatic differentiation of C++ codes for large-scale scientific computing (2006)
  10. Bücker, Martin (ed.); Corliss, George (ed.); Hovland, Paul (ed.); Naumann, Uwe (ed.); Norris, Boyana (ed.): Automatic differentiation: Applications, theory, and implementations. Selected papers based on the presentation at the 4th international conference on automatic differentiation (AD), Chicago, IL, USA, July 20--23, 2004 (2006)
  11. Castaings, W.; Dartus, D.; Honnorat, M.; Le Dimet, F.-X.; Loukili, Y.; Monnier, J.: Automatic differentiation: a tool for variational data assimilation and adjoint sensitivity analysis for flood modeling (2006)
  12. Dauvergne, Benjamin; Hascoët, Laurent: The data-flow equations of checkpointing in reverse automatic differentiation (2006)
  13. de Rocquigny, Étienne: Mastering uncertainty in industry. II: A survey of physical and numerical statistical modelling methods (2006)
  14. Hascoët, Laurent; Araya-Polo, Mauricio: The adjoint data-flow analyses: formalization, properties, and applications (2006)
  15. Naumann, Uwe; Riehme, Jan: Computing adjoints with the NAGWare Fortran 95 compiler (2006)
  16. Pascual, Valérie; Hascoët, Laurent: Extension of TAPENADE toward Fortran 95 (2006)
  17. Shampine, L. F.; Ketzscher, Robert; Forth, Shaun A.: Using AD to solve BVPs in MATLAB (2005)

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Further publications can be found at: http://www-sop.inria.fr/tropics/tapenade.html