Conveners
Progress in Computer Science: Part 1
- Garima Singh (Princeton University (US))
Progress in Computer Science: Part 2
- Max Aehle
The computational assessment of a proposed detector design usually involves Monte-Carlo simulations of how particles interact with the detector. For example, the Bergen Proton CT (pCT) collaboration uses the program GATE based on Geant4 for the development of its digital tracking calorimeter. It would be interesting to see a differentiated simulation being used as part of a differentiable...
Automatic differentiation (AD) is a practical way for computing derivatives of functions that are expressed as programs. AD has been recognized as one of the key pillars of the current machine learning (ML) revolution and has key applications in domains such as finance, computational fluid dynamics, atmospheric sciences, and engineering optimization.
This talk presents a solution for...
In this talk, we present our efforts in supporting Automatic Differentiation (AD) in RooFit, a toolkit for statistical modeling and fitting used by many HEP/NP experiments that is part of ROOT. The new AD backend improves both the performance and numeric stability of likelihood minimizations, for which we will provide several examples in this contribution. Our approach is to extend RooFit with...
Clad enables automatic differentiation (AD) for C++. It is based on LLVM compiler infrastructure and is a plugin for Clang compiler. Clad is based on source code transformation. Given C++ source code of a mathematical function, it can automatically generate C++ code for computing derivatives of the function. Clad supports a large set of C++ features including control flow statements and...
