Lars Mikelsons
Lars Mikelsons holds a diploma in Mathematics and a Ph.D. in Mechatronics. He began his professional career at Bosch Corporate Research before transitioning to academia. Currently, he is the Head of the Chair for Mechatronics at the University of Augsburg. His research focuses on Scientific Machine Learning and Mechatronic Systems Engineering, contributing to the advancement of intelligent, data-driven approaches in engineering applications.
Sessions
Hyperparameter optimization is a core workflow in machine learning and scientific computing, yet the Julia ecosystem has lacked a mature, production-ready framework comparable to the robust, battle-tested tools available in other languages. In order to bridge this gap, we present Optuna.jl a package that brings the full functionality of Optuna (by Preferred Networks, Inc.), one of the most widely adopted hyperparameter optimization frameworks, into Julia.
Functional Mock-up Units (FMUs) are widely used in industry for exchanging dynamical system models, but their black-box binary nature makes them inaccessible to traditional AD tools. Built-in derivative support in the FMI standard is limited in scope and often relies on slow finite differences. We present a novel approach: by embedding LLVM bitcode into FMU binaries during compilation, we make them accessible to Enzyme.jl, enabling fast, automatic differentiation of virtually any FMU function.