4 citations · 9 across the 4 of their papers we have counts for
6 papers
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…
Exploration of Differentiability in a Proton Computed Tomography Simulation Framework
Max Aehle, Johan Alme, Gergely Gábor Barnaföldi +47
Objective. Algorithmic differentiation (AD) can be a useful technique to numerically optimize design and algorithmic parameters by, and quantify uncertainties in, computer simulati…
Event-Based Automatic Differentiation of OpenMP with OpDiLib
Johannes Blühdorn, Max Sagebaum, Nicolas R. Gauger
We present the new software OpDiLib, a universal add-on for classical operator overloading AD tools that enables the automatic differentiation (AD) of OpenMP parallelized code. Wit…
Assign optimization for algorithmic differentiation reuse index management strategies
Max Sagebaum, Johannes Blühdorn, Nicolas R. Gauger
The identification of primal variables and adjoint variables is usually done via indices in operator overloading algorithmic differentiation tools. One approach is a linear managem…
AutoMat -- Automatic Differentiation for Generalized Standard Materials on GPUs
Johannes Blühdorn, Nicolas R. Gauger, Matthias Kabel
We propose a universal method for the evaluation of generalized standard materials that greatly simplifies the material law implementation process. By means of automatic differenti…