2025-09-09 –, Forum
The Functional Mock-up Interface (FMI) is the standard for exchanging industrial simulation models in a variety of different applications. Although sensitivity analysis for continuously differentiable systems is directly supported by the standard, for systems with state discontinuities, it is only possible to determine correct sensitivities to a limited extent. In this position paper, we investigate how sensitivity analysis for discontinuous Functional Mock-up Units (FMUs), i.e. including state and time events, works in theory and which additional steps are required to obtain correct results in practice. We further investigate that these steps are unnecessarily computationally intensive from a mathematical point of view, but cannot be implemented in a more efficient way under the current restrictions of the standard. We therefore make a concrete proposal for the new layered standard sensitivity analysis (LS-SA) that remedies the current deficits of FMI in the sensitivity analysis of discontinuous systems. In this way, LS-SA opens FMI towards a variety of next-level applications — including (scientific) machine learning and optimal control — by providing fully differentiable FMUs under high computational performance.
... is an advanced doctoral student at University of Augsburg. He completed both his Bachelor's and Master's degree in “Engineering and Computer Sciences” at this same university. His research focuses on Scientific Machine Learning, and he is specifically engaged with the area of Hybrid Modeling, the combination of simulation models – often from engineering – and novel machine learning approaches. His research includes the development of new methods in this field, but explicitly also the improvement of new and established approaches so that they can be applied in a demanding real-world environment.
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.
-1998 Ph.D. in Numerical Analysis from Lund Institute of Technology 1998 "Runge-Kutta Solution of Initial Value Problems - Methods, Algorithms and Implementation".
1999- Worked at Dassault Systemes AB (earlier Dynasim) with Modelica and Dymola.
2018- Chair of MAP-Lang (Modelica Language).
