In our paper "Liquor-HGNN: A heterogeneous graph neural network for leakage detection in water distribution networks "" we introduce the Liquor-HGNN model, a novel approach for detecting and localizing leaks in drinking water distribution networks (DWDNs) through the utilization of heterogeneous graph learning.

We, the Data Science Chair, moved to the new CAIDAS building at Emil-Fischer-Straße 50. You can find us and our new student pools on the third floor. This move concentrates the AI chairs to support our active research. You are cordially invited to visit us at our new location.

In our paper "Can Neural Networks Distinguish High-school Level Mathematical Concepts?" we evaluate the ability of various neural networks to classify the relationship between two mathematical expressions. For example, two expressions can be equivalent or one could be a derivative of the other. We show that neural networks are able to learn the underlying mathematical patterns and outperform rule-based systems.