|Authors||S. Mitusch, S. W. Funke and J. S. Dokken|
|Title||Recent developments in dolfin-adjoint|
|Project(s)||OptCutCell: Simulation-based optimisation with dynamic domains|
|Publication Type||Talks, contributed|
|Year of Publication||2019|
|Location of Talk||FEniCS'19, Washington DC|
dolfin-adjoint is a python library that enables automatic differentiation and optimization of FEniCS models by deriving and solving the corresponding first and second-order adjoint equations. In the last two years, dolfin-adjoint has been completely rewritten to accommodate the implementation of new features. For instance, we have implemented shape derivatives, which was enabled by a recent extension to UFL . We will present a highlight of the new features in dolfin-adjoint, including deformation vector and strong Dirichlet boundary condition controls. Furthermore, we present the performance of these implementations compared to the theoretical optimum. Lastly, we mention how dolfin-adjoint can be extended to support new operations.
 David A Ham, Lawrence Mitchell, Alberto Paganini, and Florian Wechsung. Automated shape differentiation in the unified form language. arXiv preprint arXiv:1808.08083, 2018.