Luisa Vollmer

h-index12
2papers

2 Papers

CEJun 11, 2025
Superstudent intelligence in thermodynamics

Rebecca Loubet, Pascal Zittlau, Marco Hoffmann et al.

In this short note, we report and analyze a striking event: OpenAI's large language model o3 has outwitted all students in a university exam on thermodynamics. The thermodynamics exam is a difficult hurdle for most students, where they must show that they have mastered the fundamentals of this important topic. Consequently, the failure rates are very high, A-grades are rare - and they are considered proof of the students' exceptional intellectual abilities. This is because pattern learning does not help in the exam. The problems can only be solved by knowledgeably and creatively combining principles of thermodynamics. We have given our latest thermodynamics exam not only to the students but also to OpenAI's most powerful reasoning model, o3, and have assessed the answers of o3 exactly the same way as those of the students. In zero-shot mode, the model o3 solved all problems correctly, better than all students who took the exam; its overall score was in the range of the best scores we have seen in more than 10,000 similar exams since 1985. This is a turning point: machines now excel in complex tasks, usually taken as proof of human intellectual capabilities. We discuss the consequences this has for the work of engineers and the education of future engineers.

HCJul 27, 2021
Time-Varying Fuzzy Contour Trees

Anna-Pia Lohfink, Frederike Gartzky, Florian Wetzels et al.

We present a holistic, topology-based visualization technique for spatial time series data based on an adaptation of Fuzzy Contour Trees. Common analysis approaches for time dependent scalar fields identify and track specific features. To give a more general overview of the data, we extend Fuzzy Contour Trees, from the visualization and simultaneous analysis of the topology of multiple scalar fields, to time dependent scalar fields. The resulting time-varying Fuzzy Contour Trees allow the comparison of multiple time steps that are not required to be consecutive. We provide specific interaction and navigation possibilities that allow the exploration of individual time steps and time windows in addition to the behavior of the contour trees over all time steps. To achieve this, we reduce an existing alignment to multiple sub-alignments and adapt the Fuzzy Contour Tree-layout to continuously reflect changes and similarities in the sub-alignments. We apply time-varying Fuzzy Contour Trees to different real-world data sets and demonstrate their usefulness.