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Section: Application Domains

Algorithmic Differentiation

Algorithmic Differentiation of programs gives sensitivities or gradients, useful for instance for :

  • 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),

  • first-order linearization of complex systems, or higher-order 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,

  • equation solving with the Newton method,

  • sensitivity analysis, propagation of truncation errors.