AuthorsS. Mitusch, S. W. Funke and J. S. Dokken
TitleRecent developments in dolfin-adjoint
AfilliationScientific Computing
Project(s)OptCutCell: Simulation-based optimisation with dynamic domains
Publication TypeTalks, contributed
Year of Publication2019
Location of TalkFEniCS'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 [1]. 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.

[1] David A Ham, Lawrence Mitchell, Alberto Paganini, and Florian Wechsung. Automated shape differentiation in the unified form language. arXiv preprint arXiv:1808.08083, 2018.

Citation Key26662