1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2023
SURF: A Generalization Benchmark for GNNs Predicting Fluid Dynamics
Stefan Künzli, Florian Grötschla, Joël Mathys +1
Simulating fluid dynamics is crucial for the design and development process, ranging from simple valves to complex turbomachinery. Accurately solving the underlying physical equati…
cs.LG2022★ 1 cited
Learning Graph Algorithms With Recurrent Graph Neural Networks
Florian Grötschla, Joël Mathys, Roger Wattenhofer
Classical graph algorithms work well for combinatorial problems that can be thoroughly formalized and abstracted. Once the algorithm is derived, it generalizes to instances of any…
cs.LG2022
Hierarchical Graph Structures for Congestion and ETA Prediction
Florian Grötschla, Joël Mathys
Traffic4cast is an annual competition to predict spatio temporal traffic based on real world data. We propose an approach using Graph Neural Networks that directly works on the roa…