13 citations · 54 across the 32 of their papers we have counts for
6 papers · 1 filter
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…
QFT-based Homogenization
Felix Givois, Matthias Kabel, Nicolas Gauger
Efficient numerical characterization is a key problem in composite material analysis. To follow accuracy improvement in image tomography, memory efficient methods of numerical char…
Physics-Informed Learning of Aerosol Microphysics
Paula Harder, Duncan Watson-Parris, Philip Stier +3
Aerosol particles play an important role in the climate system by absorbing and scattering radiation and influencing cloud properties. They are also one of the biggest sources of u…
Toward the End-to-End Optimization of Particle Physics Instruments with Differentiable Programming: a White Paper
Tommaso Dorigo, Andrea Giammanco, Pietro Vischia +33
The full optimization of the design and operation of instruments whose functioning relies on the interaction of radiation with matter is a super-human task, given the large dimensi…
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…