Marko Polic
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.
Session
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.