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ProtoIPFS by Dominik Hüser

ZipGAN is a deep learning framework for compressing and decompressing 3D Direct Numerical Simulation (DNS) datasets of turbulent flows. It uses a Super-Resolution GAN to reconstruct full-resolution DNS fields from compressed (filtered + downsampled) representations, achieving compression ratios of up to 512x while preserving fine-scale turbulence features.

Summary of the 2021/22 winter semester course "Communication Systems Engineering"

"An Optimization-based Approach To Node Role Discovery in Networks: Approximating Equitable Partitions", Michael Scholkemper and Michael T. Schaub, (2023), NeurIPS23.

Razor and Ansible

Python binding to interface VA using the Python scripting language.

Jupyter Kernel with ROOT for the Laboratory Class in Particle Physics offered at the RWTH physics institute 3B.