Authors | S. Mitusch, S. W. Funke and J. S. Dokken |
Title | Recent developments in dolfin-adjoint |
Afilliation | Scientific Computing |
Project(s) | OptCutCell: Simulation-based optimisation with dynamic domains |
Status | Published |
Publication Type | Talks, contributed |
Year of Publication | 2019 |
Location of Talk | FEniCS'19, Washington DC, USA |
Abstract | 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 Key | 26662 |