The dolfin-adjoint project automatically derives the discrete adjoint and tangent linear models from a forward model written in the Python interface to DOLFIN .
These adjoint and tangent linear models are key ingredients in many important algorithms, such as data assimilation, optimal control, sensitivity analysis, design optimisation, and error estimation. Such models have made an enormous impact in fields such as meteorology and oceanography, but their use in other scientific fields has been hampered by the great practical difficulty of their derivation and implementation. In his recent book , Naumann (2011) states that
The dolfin-adjoint project aims to solve this problem for the case where the model is implemented in the Python interface to DOLFIN.