University of Stuttgart awards publication prize: SimTech researchers among the award winners

January 29, 2021 / Sabine Sämisch

[Picture: © Bildagentur PantherMedia / Antonio Cicorella]

Each year, the University of Stuttgart awards prizes for excellent publication from the university’s ten faculties. The prize includes Euro 2,500 prize money. Amongst them are three SimTech researchers.

For Faculty 2, Christian Bleiler, SimTech PhD student, was selected for his publication on „A micro-structurally-based, multi-scale, continuum-mechanical model for the passive behavior of skeletal muscle tissue“:

Skeletal muscles are an integral part of the musculoskeletal system. The modelling and computer-based simulation of movements of the musculoskeletal system thus requires an accurate description of the mechanical properties of muscles. Tissue alterations, for example as a result of disease, can strongly influence these properties, but usually show up on much smaller length scales, such as in the underlying protein structures in the micrometre range. The multiscale continuum-mechanical model for skeletal muscle tissue presented in the publication allows the direct use of microstructural data and thus represents an important paradigm shift in skeletal muscle modelling. Experimental results from histological examinations or image-based methods can thus be used directly as input parameters for simulations. The presented model is thus an important step on the way to envisioned clinical applications.

Andrea Beck, Participating Researcher in SimTech, from Faculty 6 was selected for her publication on  "Deep neural networks for data-driven LES closure models":

The majority of all flows in technology and nature are turbulent, i.e. they are characterized by a vast range of spatial and temporal scales that we perceive as vortices or eddies. These turbulent fluctuations influence each other across the full spectrum – this in turn makes a complete resolution of all occurring interactions in a numerical simulation prohibitively costly. Here, turbulence modeling comes into play, as it reduces the computational costs by orders of magnitude and thus enables the simulation of flows for practical applications. These turbulence models are approximate in nature, and their characteristics determine the quality of the simulation results. Commonly, the development of these models is guided by physical or mathematical considerations. In our work, we complement these strategies by methods from machine learning conditioned on turbulent flow data. This allows us to derive turbulence models that can be tailored to the underlying numerical schemes and achieve a better accuracy than currently established methods.

And for Faculty 7, it was Anna Koch who was awarded with the University’s Publication Prize. Her publication on "One-Shot Verification of Dissipativity Properties From Input–Output Data" was produced in the framework of project network PN 4 of SimTech:

With the growing complexity of engineering systems, obtaining an accurate mathematical model to design suitable controllers becomes a more and more difficult and time-consuming task. On the other hand, the availability of data from systems and processes is steadily increasing. Therefore, there has been a rising interest in learning controllers directly from data without the necessity of finding a suitable mathematical model first. One of the drawbacks of many existing data-driven control approaches, however, is their lack of guarantees for stability. Such stability guarantees can, for example, be obtained via system theoretic properties. Knowledge of certain system properties can be leveraged to design controllers with stability guarantees for the closed-loop behavior. In the present publication, we introduce an approach to determine such system theoretic properties from only one input-output trajectory of an otherwise unknown linear system. For this, we develop a data-based condition for such system properties that is easy to verify without knowledge of a mathematical model.

Congratulations to all the award winners!

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