AuthorsS. W. Funke, P. E. Farrell, D. A. Ham and M. E. Rognes
TitleDolfin-adjoint: Automatic adjoint models for FEniCS
AfilliationScientific Computing, , Scientific Computing
Project(s)Center for Biomedical Computing (SFF)
Publication TypePoster
Year of Publication2015
Secondary TitleThe 8th International Congress on Industrial and Applied Mathematics
PublisherThe 8th International Congress on Industrial and Applied Mathematics
Place PublishedBeijing, China

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.

Citation Key23582

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