AD-based perturbation methods for uncertainties and errors

International audienceThe progress of Automatic Differentiation ({\bf AD}) and its impact on perturbation methods is the object of this paper. AD studies show an important activity for developing methods addressing the management of modern CFD kernels, taking into account the language evolution, and intensive parallel computing. The evaluation of a posteriori error analysis and of resulting correctors will be addressed. Recents works in the AD-based contruction of second-derivatives for building reduced-order models based on a Taylor formula will be presented on the test case of a steady compressible flow around an aircraft