Section: Application Domains
Algorithmic Differentiation
Algorithmic Differentiation of programs gives sensitivities or gradients, useful for instance forĀ :

optimum shape design under constraints, multidisciplinary optimization, and more generally any algorithm based on local linearization,

inverse problems, such as parameter estimation and in particular 4Dvar data assimilation in climate sciences (meteorology, oceanography),

firstorder linearization of complex systems, or higherorder simulations, yielding reduced models for simulation of complex systems around a given state,

adaption of parameters for classification tools such as Machine Learning systems, in which Adjoint Differentiation is also known as backpropagation.

mesh adaptation and mesh optimization with gradients or adjoints,