A multimesh graph-neural-network surrogate conditioned on discharge and trained with pushforward produces accurate 6-hour flood forecasts in 0.4 s on GPU versus 180 min on 56 CPU cores for the reference Telemac2D model.
In- terpretable physics-informed graph neural networks for flood forecasting.Computer-Aided Civil and Infrastructure Engineering, 40:2629–2649
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Toward an Operational GNN-Based Multimesh Surrogate for Fast Flood Forecasting
A multimesh graph-neural-network surrogate conditioned on discharge and trained with pushforward produces accurate 6-hour flood forecasts in 0.4 s on GPU versus 180 min on 56 CPU cores for the reference Telemac2D model.