8 citations · 31 across the 19 of their papers we have counts for
4 papers · 1 filter
Hybrid parallel discrete adjoints in SU2
Johannes Blühdorn, Pedro Gomes, Max Aehle +1
The open-source multiphysics suite SU2 features discrete adjoints by means of operator overloading automatic differentiation (AD). While both primal and discrete adjoint solvers su…
Integrating Enzyme-generated functions into CoDiPack
M. Sagebaum, M. Aehle, N. R. Gauger
In operator overloading algorithmic differentiation, it can be beneficial to create custom derivative functions for some parts of the code base. For manual implementations of the d…
Reverse-Mode Automatic Differentiation of Compiled Programs
Max Aehle, Johannes Blühdorn, Max Sagebaum +1
Tools for algorithmic differentiation (AD) provide accurate derivatives of computer-implemented functions for use in, e. g., optimization and machine learning (ML). However, they o…
Forward-Mode Automatic Differentiation of Compiled Programs
Max Aehle, Johannes Blühdorn, Max Sagebaum +1
Algorithmic differentiation (AD) is a set of techniques that provide partial derivatives of computer-implemented functions. Such a function can be supplied to state-of-the-art AD t…