In this study flows over and through modelled aquatic plant canopies are investigated to better understand the interaction between the outer flow and the interior of the canopy. This is relevant for the resistance exerted by the canopy and the exchange of oxygen, pollutants, etc. between flow and canopy. Here, very detailed numerical simulations are conducted to resolve the canopy with all individual blades with an unprecedented detail. The configurations studied are densely arranged, highly flexible ribbons, which overall represent a situation very close to real seagrass meadows, much closer than in other studies. Unexpected, for example, is the observation that the blades move quite far up-wards and even further in horizontal direction.

Converging calculations is a common need in the ab initio materials-science community.
This tedious and resource-intensive process can be largely avoided if well-validated
recommendations are available. In order to create a recommender system to assist
users, benchmark data are required. This project addresses this need. It evaluates the
convergence behavior of electronic properties for a dataset of 10 materials that are
promising for optoelectronic applications.

Computational tools such as Molecular Dynamics (MD) have revolutionized the way we study biomolecules; however, they are severely limited by the computational cost of running simulations on biological time- and length-scales. Various coarse-grained (CG) models have been developed which rely on simpler representations of molecular systems than atomistic MD. While these models are difficult to configure using physical intuition, we have shown that by using state-of-the-art machine learning methods, it is possible to design accurate and efficient CG models which can correctly reproduce protein dynamics. By enhancing both our training dataset and network architecture, we hope to produce a “universal” CG model to study biological systems.

Within Text+, the NFDI consortium dedicated to building and providing infrastructure for the field of digital humanities, high performance computing (HPC) is gaining ground quickly. With the arrival of large language models (LLMs), the motivation for providing HPC infrastructure increased decisively. As a consequence, the first HPC service was established, easing the way for developments in several other areas where access to HPC is needed for enabling solutions otherwise not feasible. Examples of HPC use in Text+ are the Text+ LLM service and the NLP tool MONAPipe.

An optimum and selective materials design based on computational approaches is essential in order to avoid time-consuming and expensive experiments and drive novel applications in sensing, catalysis, and electronic components. Within this concept, (a) the optimization of nanoporous materials made of functionalized gold surfaces with self-assembled monolayers, as well as (b) the design of alloying materials were investigated. The investigations are directed towards (a) heterocatalysis and biosensing, as well as (b) alloys for strong and highly conducting electronic components were seeked. This research was performed using quantum-mechanical and atomistic simulations occasionally in combination with Machine Learning approaches.

OpenGPT-X has set a goal to create and train open large language models (LLM) for European languages. Existing language models focus primarily on the English language, and hence perform unfavourably when used for any of the other commonly spoken European languages.
From large-scale benchmarking of multilingual LLMs to introducing Teuken-7B models, our research uncovers how tokenization and balanced datasets enhance cross-lingual performance. Join us in exploring transparent and reproducible innovations shaping the future of multilingual AI.

For the simulation of turbulent dispersed liquid-liquid flows at large scales, coalescence and breakup of droplets is approximated with sub-grid scale closures. For these closures, the root mean square (RMS) droplet fluctuation velocity Urms,d is a decisive input quantity. Recently, Solsvik & Jakobsen [1] proposed an enhanced model to predict Urms,d, which has not been verified yet. Hence, Direct Numerical Simulations (DNS) together with a Volume-of-Fluid (VoF) approach were employed to study the motion of single droplets in a Forced Homogeneous Isotropic Turbulent (FHIT) flow. A parameter study was conducted to investigate the effect of the initial droplet diameter D on Urms,d, and the DNS results were used to assess the model from [1].

Large Language Models (LLMs) are continuously being developed and improved, and there is no shortage of benchmarks that quantify how well they work; LLM benchmarking is indeed a long-standing practice especially in the NLP research community. However, the majority of these benchmarks are not designed for German-language LLMs. We assembled a broad Natural Language Understanding benchmark suite for the German language and evaluated a wide array of existing German-capable models.
This allows us to comprehensively chart the landscape of German LLMs.

Centrifugal pumps are employed in various industrial and engineering applications to transport two-phase mixtures as liquid and non-condensable gas. Several examples of the two-phase pump operation can be found, e.g., in the chemical and process industry or geothermal power stations. Predicting two-phase flows in centrifugal pumps with state-of-the-art computational fluid dynamic (CFD) methods is only possible by accepting significant uncertainties.

Every year we, as the Formula Student Team of RWTH Aachen University, develop a completely new electric race car and revise a previous car to be able to drive autonomously. For our Aerodynamics team, the electric vehicle is the main focus. We try to find the best geometries for our car within the regulatory constraints and while keeping performance compromises with other design areas in mind.