Reverse Automatic Differentiation for Optimum Design: from Adjoint State Assembly to Gradient Computation
Résumé
The utilization of reverse mode Automatic Differentiation to the adjoint method for solving an Optimal Design problem is described. Using the reverse mode, we obtain the adjoint system residual in a rather efficient way. But memory requirements may be very large. The family of programs to differentiate involves many independant calculations, typically in parallel loops. Then we propose to apply a reverse differentiation «by iteration». This demands much less memory storage. This methods is used for the computing of the adjoint state and gradient related to the Optimal Design problem.