2026-08-14 –, Room 3
Using the Functional Mock-Up Interface (FMI), we can handle and exchange big simulation models. It seems only logical to integrate this standard into our favorite programming language. Our open-source journey of FMI.jl started almost exactly 5 years ago with this goal in mind: Blur the boundaries between Julia and FMI. In this talk, we want to give a broad overview over what is possible with FMUs in Julia today – with live programs that fit a single slide each.
One of the biggest obstacles in the transition from demo application to the task that we actually want to solve is often the dimension of the simulation model. The bigger (and more complex) the system of equations becomes, the less handy it is. To tackle this issue, various modeling tools have been developed for different domains over time, and the multitude of tools has created a new problem: The exchange of models between the tools is not trivial – but definitely necessary!
The Functional Mock-Up Interface (FMI) was developed with the aim of eliminating this problem in the field of engineering industry. And because this worked out quite well, other domains beyond engineering adapted the standard. The idea was quite simple: Define an interface, that allows for the creation of simulation models that can be imported and exported by a variety of simulation tools. Models that implement this standard are known as Functional Mock-Up Units (FMUs).
So, anyone who works with large simulation models from industry has probably had contact with FMI at one time or another. And it seems only logical to integrate this standard into our favorite programming language. Our open-source journey of FMI.jl started almost exactly 5 years ago with this goal in mind: Blur the boundaries between Julia and FMI. Since then, the core library opened up to many new and interesting application domains. In this talk, we want to give a broad overview over what is possible with FMUs in Julia today – with live programs that fit a single slide each.
With the combined power of Julia and FMI, dealing with big simulation models becomes as easy as playing around with small demo systems.
... 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.