In this paper we demonstrate a new technique for deriving discrete adjoint and tangent
linear models of a finite element model. The technique is significantly more efficient and …

Derivatives are key to numerous science, engineering, and machine learning applications.
While existing tools generate derivatives of programs in a single language, modern parallel …

The open-source multiphysics suite SU2 features discrete adjoints by means of operator
overloading automatic differentiation (AD). While both primal and discrete adjoint solvers …

OpenFOAM is a powerful Open-Source (GPLv3) Computational Fluid Dynamics tool box
with a rising adoption in both academia and industry due to its continuously growing set of …

Algorithmic differentiation (AD) is a tool for generating discrete adjoint solvers, which
efficiently compute gradients of functions with many inputs, for example for use in gradient …

Automatic differentiation (AD) is a technique for computing the derivative of function F: R n→
R m defined by a computer program. Modern applications of AD, such as machine learning …

Numerical programs often use parallel programming techniques such as OpenMP to
compute the program's output values as efficient as possible. In addition, derivative values of …

Reverse-mode algorithmic differentiation (AD) is an established method for obtaining adjoint
derivatives of computer simulation applications. In computational fluid dynamics (CFD) …

In shared-memory parallel automatic differentiation, inputs that are shared among
simultaneous thread-local preaccumulations lead to data races if Jacobians are …

Over the last decade an effort between MTU Areo Engines, DLR Cologne and the Chair for
Scientific Computing at the TU Kaiserslautern has been made to create an adjoint solver for …