Evaluating derivatives: principles and techniques of algorithmic differentiation
Evaluating derivatives: principles and techniques of algorithmic differentiation
Automatic differentiation of algorithms: from simulation to optimization
Automatic differentiation of algorithms: from simulation to optimization
Adifor 2.0: Automatic Differentiation of Fortran 77 Programs
IEEE Computational Science & Engineering
Future Generation Computer Systems
Generating efficient derivative code with TAF
Future Generation Computer Systems
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Given a numerical simulation of the near wake of an airfoil, automatic differentiation is used to accurately compute the sensitivities of the Mach number with respect to the angle of attack. Such sensitivity information is crucial when integrating a pure simulation code into an optimization framework involving a gradient-based optimization technique. In this note, the ADIFOR system implementing the technology of automatic differentiation for functions written in Fortran 77 is used to mechanically transform a given flow solver called TFS into a new program capable of computing the original simulation and the desired derivatives in a simultaneous fashion. Numerical experiments of derivatives obtained from automatic differentiation and finite differences approximations are reported.