Third MODE Workshop on Differentiable Programming for Experiment Design

Session

Progress in Computer Science

24 Jul 2023, 16:00

Princeton University

Conveners

Progress in Computer Science: Part 1

• Garima Singh (Princeton University (US))

Progress in Computer Science: Part 2

• Max Aehle

110. Differentiating GATE/Geant4 with Derivgrind

Max Aehle

24/07/2023, 16:00

Computer Science

Talk

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...

118. Reverse-Mode Automatic Differentiation in Futhark Language

Prof. Cosmin E. Oancea (DIKU, University of Copenhagen)

24/07/2023, 16:30

Computer Science

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...

113. Making Likelihood Calculations Fast Using Automatic Differentiation in RooFit

Garima Singh (Princeton University (US))

25/07/2023, 15:40

Computer Science

Talk

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...

105. Efficient C++ Derivatives Through Source Transformation AD With Clad

Vassil Vasilev (Princeton University (US))

25/07/2023, 16:10

Computer Science

Talk

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...

131. Applications of Automatic Differentiation in Error Estimation and Beyond

Harshitha Menon (LLNL)