
This dataset provides a set of 4 simulation tasks for testing long-range interactions in Graph Neural Simulators, used in our study on improving long-range interactions via Hamiltonian dynamics. The dataset is released to support reproducible evaluation and benchmarking of long-horizon information propagation in graph-based simulators. Code / training pipeline: https://github.com/thobotics/neural_pde_matching Citation If you use this dataset, please cite: @inproceedings{ hoang2026igns, title={Improving Long-Range Interactions in Graph Neural Simulators via Hamiltonian Dynamics}, author={Tai Hoang and Alessandro Trenta and Alessio Gravina and Niklas Freymuth and Philipp Becker and Davide Bacciu and Gerhard Neumann}, booktitle={The Fourteenth International Conference on Learning Representations}, year={2026}, url={https://openreview.net/forum?id=x66u6TEDUw},}
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
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| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
