Abstract
Accurately forecasting dynamic processes on graphs, such as traffic flow or disease spread, remains a challenge. While Graph Neural Networks (GNNs) excel at modeling and forecasting spatio-temporal data, they often lack the ability to directly incorporate underlying physical laws. This work presents TG-PhyNN, a novel Temporal Graph Physics-Informed Neural Network framework. TG-PhyNN leverages the power of GNNs for graph-based modeling while simultaneously incorporating physical constraints as a guiding principle during training. Our findings demonstrate that TG-PhyNN significantly outperforms traditional forecasting models (e.g., GRU, LSTM, GAT) on real-world spatio-temporal datasets like PedalMe (traffic flow), COVID-19 spread, and Chickenpox outbreaks. These datasets are all governed by well-defined physical principles. This paves the way for improved forecasting in areas like traffic flow prediction, disease outbreak prediction, and potentially other fields where physics plays a crucial role.
Z. Elabid and L. Sasal—These authors contributed equally to this work.
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The support of TotalEnergies is fully acknowledged.
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Elabid, Z., Sasal, L., Busby, D., Hadid, A. (2025). TG-PhyNN: An Enhanced Physically-Aware Graph Neural Network Framework for Forecasting Spatio-Temporal Data. In: Hadfi, R., Anthony, P., Sharma, A., Ito, T., Bai, Q. (eds) PRICAI 2024: Trends in Artificial Intelligence. PRICAI 2024. Lecture Notes in Computer Science(), vol 15282. Springer, Singapore. https://doi.org/10.1007/978-981-96-0119-6_4
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