4 citations · 11 across the 10 of their papers we have counts for
11 papers
Exploring the Boundaries of Differentiable Radiation Transport and Detector Simulation
Jeffrey Krupa, Yiyang Zhao, Mihaly Novak +9
We present an application of automatic differentiation for particle transport through matter using a Geant4-like radiation transport simulation with a full electromagnetic physics…
Algorithmic differentiation for domain specific languages in C++ with expression templates
Max Sagebaum, Nicolas R. Gauger
The application of operator overloading algorithmic differentiation (AD) to computer programs in order to compute the derivative is quite common. But, the replacement of the underl…
Adding complex numbers to expression template algorithmic differentiation tools
Max Sagebaum, Nicolas R. Gauger
Operator overloading algorithmic differentiation (AD) tools are usually only developed for floating-point values. Algorithmic optimization for, e.g., linear systems solvers or matr…
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…