2026-08-12 –, Room 6
Automatic Differentiation (AD) methods present an efficient way for computing function derivatives, however the sheer amount of the implemented methods can be overwhelming.
Chosing the correct method for the task can have crucial impact on the performance[1], hence understanding the differences between them and their use cases is beneficial.
The idea for this talk arose after implementing the Gauss–Kronrod adjoint. While there are many introductory talks on automatic differentiation (AD), there are few/none that focus on the practical differences between specific methods.
Therefore, this talk is not intended as an introduction to AD. Instead, it will explore when and why to use forward- or reverse-mode AD, as well as when and why to choose different adjoint sensitivity algorithms.
The topic contains enough material for a 30-minute talk. However, I believe focusing on the core insights would be more engaging and a better use of the audience’s time, making a 15-minute format ideal. Those interested in further details can follow Chris Rackauckas’s lecture [2], read the SciMLSensitivity documentation, or reach out to me after the talk.
I am an M.Sc. student in Mathematics in Science and Engineering at the Technical University of Munich, currently focusing on mathematical modeling as a working student at JuliaHub.
I recently completed my Master’s thesis, in which I formally introduced the Gauss–Legendre and Gauss–Kronrod adjoints and implemented the latter, under the supervision of Prof. Dr. Oliver Junge and Dr. Christopher Rackauckas.
I discovered the Julia programming language three years ago and have used it as my primary programming language ever since. While I am passionate about many areas, my main research interests lie in mathematical modeling, adjoint sensitivity analysis, and vehicle-related systems.