Adjoint and tangent linear models form the basis of many numerical techniques, including sensitivity
analysis, optimization and stability analysis. The implementation of adjoint models for nonlinear or time-
dependent models are notoriously challenging: the manual approach is time-consuming and traditional
automatic differentiation tools lack robustness and performance.
dolfin-adjoint solves this problem by automatically analyzing the high-level mathematical structure
inherent in finite element methods. It raises the traditional abstraction of algorithmic differentiation
from the level of individual floating point operations to that of whole systems of differential equations.
This approach delivers a number of advantages over the previous state-of-the-art: robust hands-off
automation of adjoint model derivation, optimal computational efficiency, and native parallel support.